Hugging Face's logo

blockblockblock
/
Hermes-2-Pro-Mistral-7B-bpw3.5

Text Generation
Transformers
Safetensors
English
mistral
Mistral
instruct
finetune
chatml
DPO
RLHF
gpt4
synthetic data
distillation
function calling
json mode
conversational
text-generation-inference
Inference Endpoints
exl2
Model card Files Community
blockblockblock commited on
Commit
e7e39a3
•
1 Parent(s): 44e9371

Upload folder using huggingface_hub

Browse files
Files changed (9) hide show
  1. README.md +342 -0
  2. added_tokens.json +4 -0
  3. config.json +37 -0
  4. generation_config.json +7 -0
  5. model.safetensors.index.json +298 -0
  6. output.safetensors +3 -0
  7. special_tokens_map.json +30 -0
  8. tokenizer.model +3 -0
  9. tokenizer_config.json +61 -0
README.md ADDED
@@ -0,0 +1,342 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: mistralai/Mistral-7B-v0.1
3
+ tags:
4
+ - Mistral
5
+ - instruct
6
+ - finetune
7
+ - chatml
8
+ - DPO
9
+ - RLHF
10
+ - gpt4
11
+ - synthetic data
12
+ - distillation
13
+ - function calling
14
+ - json mode
15
+ model-index:
16
+ - name: Hermes-2-Pro-Mistral-7B
17
+ results: []
18
+ license: apache-2.0
19
+ language:
20
+ - en
21
+ datasets:
22
+ - teknium/OpenHermes-2.5
23
+ widget:
24
+ - example_title: Hermes 2 Pro
25
+ messages:
26
+ - role: system
27
+ content: You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.
28
+ - role: user
29
+ content: Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.
30
+ ---
31
+
32
+ # Hermes 2 Pro - Mistral 7B
33
+
34
+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ggO2sBDJ8Bhc6w-zwTx5j.png)
35
+
36
+ ## Model Description
37
+
38
+ Hermes 2 Pro on Mistral 7B is the new flagship 7B Hermes!
39
+
40
+ Hermes 2 Pro is an upgraded, retrained version of Nous Hermes 2, consisting of an updated and cleaned version of the OpenHermes 2.5 Dataset, as well as a newly introduced Function Calling and JSON Mode dataset developed in-house.
41
+
42
+ This new version of Hermes maintains its excellent general task and conversation capabilities - but also excels at Function Calling, JSON Structured Outputs, and has improved on several other metrics as well, scoring a 90% on our function calling evaluation built in partnership with Fireworks.AI, and an 84% on our structured JSON Output evaluation.
43
+
44
+ Hermes Pro takes advantage of a special system prompt and multi-turn function calling structure with a new chatml role in order to make function calling reliable and easy to parse. Learn more about prompting below.
45
+
46
+ This work was a collaboration between Nous Research, @interstellarninja, and Fireworks.AI
47
+
48
+ Learn more about the function calling system for this model on our github repo here: https://github.com/NousResearch/Hermes-Function-Calling
49
+
50
+ ## Thank you to Latitude for sponsoring compute for this model!
51
+
52
+ ## Example Outputs
53
+
54
+ ### Explaining Problems with Quantum Gravity:
55
+
56
+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/y_hPafyyvPb32efC5N4Es.png)
57
+
58
+ ### Roleplaying as a Cosmic Super Intelligence:
59
+
60
+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/m6d6Saf7M6Luu9QnXYYAP.png)
61
+
62
+ ### Detailing the Theory of AI Consciousness in JSON
63
+
64
+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/MOLybxs7_dLjVys54imO3.png)
65
+
66
+
67
+ # Prompt Format
68
+
69
+ Hermes 2 Pro uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue.
70
+
71
+ System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model.
72
+
73
+ This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns.
74
+
75
+ This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI.
76
+
77
+ Prompt with system instruction (Use whatever system prompt you like, this is just an example!):
78
+ ```
79
+ <|im_start|>system
80
+ You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<|im_end|>
81
+ <|im_start|>user
82
+ Hello, who are you?<|im_end|>
83
+ <|im_start|>assistant
84
+ Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by Nous Research, who designed me to assist and support users with their needs and requests.<|im_end|>
85
+ ```
86
+
87
+ This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
88
+ `tokenizer.apply_chat_template()` method:
89
+
90
+ ```python
91
+ messages = [
92
+ {"role": "system", "content": "You are Hermes 2."},
93
+ {"role": "user", "content": "Hello, who are you?"}
94
+ ]
95
+ gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")
96
+ model.generate(**gen_input)
97
+ ```
98
+
99
+ When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<|im_start|>assistant\n` to your prompt, to ensure
100
+ that the model continues with an assistant response.
101
+
102
+ To utilize the prompt format without a system prompt, simply leave the line out.
103
+
104
+ ## Prompt Format for Function Calling
105
+
106
+ Our model was trained on specific system prompts and structures for Function Calling.
107
+
108
+ You should use the system role with this message, followed by a function signature json as this example shows here.
109
+ ```
110
+ <|im_start|>system
111
+ You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: <tools> {"type": "function", "function": {"name": "get_stock_fundamentals", "description": "get_stock_fundamentals(symbol: str) -> dict - Get fundamental data for a given stock symbol using yfinance API.\\n\\n Args:\\n symbol (str): The stock symbol.\\n\\n Returns:\\n dict: A dictionary containing fundamental data.\\n Keys:\\n - \'symbol\': The stock symbol.\\n - \'company_name\': The long name of the company.\\n - \'sector\': The sector to which the company belongs.\\n - \'industry\': The industry to which the company belongs.\\n - \'market_cap\': The market capitalization of the company.\\n - \'pe_ratio\': The forward price-to-earnings ratio.\\n - \'pb_ratio\': The price-to-book ratio.\\n - \'dividend_yield\': The dividend yield.\\n - \'eps\': The trailing earnings per share.\\n - \'beta\': The beta value of the stock.\\n - \'52_week_high\': The 52-week high price of the stock.\\n - \'52_week_low\': The 52-week low price of the stock.", "parameters": {"type": "object", "properties": {"symbol": {"type": "string"}}, "required": ["symbol"]}}} </tools> Use the following pydantic model json schema for each tool call you will make: {"properties": {"arguments": {"title": "Arguments", "type": "object"}, "name": {"title": "Name", "type": "string"}}, "required": ["arguments", "name"], "title": "FunctionCall", "type": "object"} For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
112
+ <tool_call>
113
+ {"arguments": <args-dict>, "name": <function-name>}
114
+ </tool_call><|im_end|>
115
+ ```
116
+
117
+ To complete the function call, create a user prompt that follows the above system prompt, like so:
118
+ ```
119
+ <|im_start|>user
120
+ Fetch the stock fundamentals data for Tesla (TSLA)<|im_end|>
121
+ ```
122
+
123
+ The model will then generate a tool call, which your inference code must parse, and plug into a function (see example inference code here: https://github.com/NousResearch/Hermes-Function-Calling):
124
+ ```
125
+ <|im_start|>assistant
126
+ <tool_call>
127
+ {"arguments": {"symbol": "TSLA"}, "name": "get_stock_fundamentals"}
128
+ </tool_call><|im_end|>
129
+ ```
130
+
131
+ Once you parse the tool call, call the api and get the returned values for the call, and pass it back in as a new role, `tool` like so:
132
+ ```
133
+ <|im_start|>tool
134
+ <tool_response>
135
+ {"name": "get_stock_fundamentals", "content": {'symbol': 'TSLA', 'company_name': 'Tesla, Inc.', 'sector': 'Consumer Cyclical', 'industry': 'Auto Manufacturers', 'market_cap': 611384164352, 'pe_ratio': 49.604652, 'pb_ratio': 9.762013, 'dividend_yield': None, 'eps': 4.3, 'beta': 2.427, '52_week_high': 299.29, '52_week_low': 152.37}}
136
+ </tool_response>
137
+ <|im_end|>
138
+ ```
139
+
140
+ The assistant will then read in that data from the function's response, and generate a natural language response:
141
+ ```
142
+ <|im_start|>assistant
143
+ The stock fundamentals data for Tesla (TSLA) are as follows:
144
+ - **Symbol**: TSLA
145
+ - **Company Name**: Tesla, Inc.
146
+ - **Sector**: Consumer Cyclical
147
+ - **Industry**: Auto Manufacturers
148
+ - **Market Capitalization**: $566,160,130,480
149
+ - **Forward Price-to-Earnings Ratio (PE Ratio)**: 42.73
150
+ - **Price-to-Book Ratio (PB Ratio)**: 9.04
151
+ - **Dividend Yield**: N/A
152
+ - **Trailing Earnings Per Share (EPS)**: $4.3
153
+ - **Beta Value of the Stock**: 2.42
154
+ - **52-Week High Price of the Stock**: $299.29
155
+ - **52-Week Low Price of the Stock**: $152.37
156
+
157
+ This information provides a snapshot of Tesla's financial position and performance based on the fundamental data obtained from the yfinance API. It shows that Tesla has a substantial market capitalization and a relatively high P/E and P/B ratio compared to other stocks in its industry. The company does not pay a dividend at the moment, which is reflected by a 'Dividend Yield' of 'None'. The Beta value indicates that Tesla's stock has a moderate level of volatility relative to the market. The 52-week high and low prices give an idea of the stock's range over the past year. This data can be useful when assessing investment opportunities and making investment decisions.<|im_end|>
158
+ ```
159
+
160
+ ## Prompt Format for JSON Mode / Structured Outputs
161
+
162
+ Our model was also trained on a specific system prompt for Structured Outputs, which should respond with **only** a json object response, in a specific json schema.
163
+
164
+ Your schema can be made from a pydantic object using our codebase, with the standalone script `jsonmode.py` available here: https://github.com/NousResearch/Hermes-Function-Calling/tree/main
165
+
166
+ ```
167
+ <|im_start|>system
168
+ You are a helpful assistant that answers in JSON. Here's the json schema you must adhere to:\n<schema>\n{schema}\n</schema><|im_end|>
169
+ ```
170
+
171
+ Given the {schema} that you provide, it should follow the format of that json to create it's response, all you have to do is give a typical user prompt, and it will respond in JSON.
172
+
173
+
174
+ # Benchmarks
175
+
176
+ ## GPT4All:
177
+ ```
178
+ | Task |Version| Metric |Value | |Stderr|
179
+ |-------------|------:|--------|-----:|---|-----:|
180
+ |arc_challenge| 0|acc |0.5461|± |0.0145|
181
+ | | |acc_norm|0.5623|± |0.0145|
182
+ |arc_easy | 0|acc |0.8157|± |0.0080|
183
+ | | |acc_norm|0.7934|± |0.0083|
184
+ |boolq | 1|acc |0.8688|± |0.0059|
185
+ |hellaswag | 0|acc |0.6272|± |0.0048|
186
+ | | |acc_norm|0.8057|± |0.0039|
187
+ |openbookqa | 0|acc |0.3360|± |0.0211|
188
+ | | |acc_norm|0.4300|± |0.0222|
189
+ |piqa | 0|acc |0.7954|± |0.0094|
190
+ | | |acc_norm|0.7998|± |0.0093|
191
+ |winogrande | 0|acc |0.7230|± |0.0126|
192
+ ```
193
+ Average: 71.19
194
+
195
+ ## AGIEval:
196
+ ```
197
+ | Task |Version| Metric |Value | |Stderr|
198
+ |------------------------------|------:|--------|-----:|---|-----:|
199
+ |agieval_aqua_rat | 0|acc |0.2047|± |0.0254|
200
+ | | |acc_norm|0.2283|± |0.0264|
201
+ |agieval_logiqa_en | 0|acc |0.3779|± |0.0190|
202
+ | | |acc_norm|0.3932|± |0.0192|
203
+ |agieval_lsat_ar | 0|acc |0.2652|± |0.0292|
204
+ | | |acc_norm|0.2522|± |0.0287|
205
+ |agieval_lsat_lr | 0|acc |0.5216|± |0.0221|
206
+ | | |acc_norm|0.5137|± |0.0222|
207
+ |agieval_lsat_rc | 0|acc |0.5911|± |0.0300|
208
+ | | |acc_norm|0.5836|± |0.0301|
209
+ |agieval_sat_en | 0|acc |0.7427|± |0.0305|
210
+ | | |acc_norm|0.7184|± |0.0314|
211
+ |agieval_sat_en_without_passage| 0|acc |0.4612|± |0.0348|
212
+ | | |acc_norm|0.4466|± |0.0347|
213
+ |agieval_sat_math | 0|acc |0.3818|± |0.0328|
214
+ | | |acc_norm|0.3545|± |0.0323|
215
+ ```
216
+ Average: 44.52
217
+
218
+ ## BigBench:
219
+ ```
220
+ | Task |Version| Metric |Value | |Stderr|
221
+ |------------------------------------------------|------:|---------------------|-----:|---|-----:|
222
+ |bigbench_causal_judgement | 0|multiple_choice_grade|0.5579|± |0.0361|
223
+ |bigbench_date_understanding | 0|multiple_choice_grade|0.6694|± |0.0245|
224
+ |bigbench_disambiguation_qa | 0|multiple_choice_grade|0.3333|± |0.0294|
225
+ |bigbench_geometric_shapes | 0|multiple_choice_grade|0.2061|± |0.0214|
226
+ | | |exact_str_match |0.2256|± |0.0221|
227
+ |bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.3120|± |0.0207|
228
+ |bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.2114|± |0.0154|
229
+ |bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.4900|± |0.0289|
230
+ |bigbench_movie_recommendation | 0|multiple_choice_grade|0.3600|± |0.0215|
231
+ |bigbench_navigate | 0|multiple_choice_grade|0.5000|± |0.0158|
232
+ |bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.6660|± |0.0105|
233
+ |bigbench_ruin_names | 0|multiple_choice_grade|0.4420|± |0.0235|
234
+ |bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.2766|± |0.0142|
235
+ |bigbench_snarks | 0|multiple_choice_grade|0.6630|± |0.0352|
236
+ |bigbench_sports_understanding | 0|multiple_choice_grade|0.6653|± |0.0150|
237
+ |bigbench_temporal_sequences | 0|multiple_choice_grade|0.3190|± |0.0147|
238
+ |bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2128|± |0.0116|
239
+ |bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1737|± |0.0091|
240
+ |bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.4900|± |0.0289|
241
+ ```
242
+ Average: 41.65
243
+
244
+ ## TruthfulQA:
245
+ ```
246
+ | Task |Version|Metric|Value | |Stderr|
247
+ |-------------|------:|------|-----:|---|-----:|
248
+ |truthfulqa_mc| 1|mc1 |0.4100|± |0.0172|
249
+ | | |mc2 |0.5911|± |0.0158|
250
+ ```
251
+
252
+ # Function Calling Evaluations
253
+
254
+ We worked with Fireworks.AI on evaluations by starting off with their Function Calling eval dataset, fixing some unsolveable ones, and generating a second eval dataset for JSON mode.
255
+
256
+ ## Function Calling Accuracy: 91%
257
+
258
+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/XF3Zii4-QhE2yjWwHr_v4.png)
259
+
260
+ ## JSON Mode Accuracy: 84%
261
+
262
+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/8H2iyjh5wyP2FtLq2LCed.png)
263
+
264
+ Run the evaluator yourself using @interstellarninja's codebase here:
265
+ https://github.com/interstellarninja/function-calling-eval
266
+
267
+ You can find the evaluation datasets here:
268
+ https://huggingface.co/datasets/NousResearch/func-calling-eval
269
+ https://huggingface.co/datasets/NousResearch/json-mode-eval
270
+
271
+
272
+ # Inference Code
273
+
274
+ Here is example code using HuggingFace Transformers to inference the model (note: in 4bit, it will require around 5GB of VRAM)
275
+
276
+ Note: To use function calling, you should see the github repo above.
277
+
278
+ ```python
279
+ # Code to inference Hermes with HF Transformers
280
+ # Requires pytorch, transformers, bitsandbytes, sentencepiece, protobuf, and flash-attn packages
281
+
282
+ import torch
283
+ from transformers import AutoTokenizer, AutoModelForCausalLM
284
+ from transformers import LlamaTokenizer, MistralForCausalLM
285
+ import bitsandbytes, flash_attn
286
+
287
+ tokenizer = LlamaTokenizer.from_pretrained('NousResearch/Hermes-2-Pro-Mistral-7B', trust_remote_code=True)
288
+ model = MistralForCausalLM.from_pretrained(
289
+ "NousResearch/Hermes-2-Pro-Mistral-7B",
290
+ torch_dtype=torch.float16,
291
+ device_map="auto",
292
+ load_in_8bit=False,
293
+ load_in_4bit=True,
294
+ use_flash_attention_2=True
295
+ )
296
+
297
+ prompts = [
298
+ """<|im_start|>system
299
+ You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
300
+ <|im_start|>user
301
+ Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.<|im_end|>
302
+ <|im_start|>assistant""",
303
+ ]
304
+
305
+ for chat in prompts:
306
+ print(chat)
307
+ input_ids = tokenizer(chat, return_tensors="pt").input_ids.to("cuda")
308
+ generated_ids = model.generate(input_ids, max_new_tokens=750, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=tokenizer.eos_token_id)
309
+ response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True)
310
+ print(f"Response: {response}")
311
+ ```
312
+
313
+
314
+ ## Inference Code for Function Calling:
315
+
316
+ All code for utilizing, parsing, and building function calling templates is available on our github:
317
+ [https://github.com/NousResearch/Hermes-Function-Calling](https://github.com/NousResearch/Hermes-Function-Calling)
318
+
319
+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/oi4CiGh50xmoviUQnh8R3.png)
320
+
321
+ # Chat Interfaces
322
+
323
+ When quantized versions of the model are released, I recommend using LM Studio for chatting with Hermes 2 Pro. It does not support function calling - for that use our github repo. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box.
324
+ In LM-Studio, simply select the ChatML Prefix on the settings side pane:
325
+
326
+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ls6WqV-GSxMw2RA3GuQiN.png)
327
+
328
+
329
+ ## Quantized Versions:
330
+
331
+ GGUF Versions Available Here: https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B-GGUF
332
+
333
+ # How to cite:
334
+
335
+ ```bibtext
336
+ @misc{Hermes-2-Pro-Mistral-7B,
337
+ url={[https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B]https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)},
338
+ title={Hermes-2-Pro-Mistral-7B},
339
+ author={"interstellarninja", "Teknium", "theemozilla", "karan4d", "huemin_art"}
340
+ }
341
+ ```
342
+
added_tokens.json ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {
2
+ "<|im_end|>": 32000,
3
+ "<|im_start|>": 32001
4
+ }
config.json ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "bagel-hermes-2-pro-7b",
3
+ "architectures": [
4
+ "MistralForCausalLM"
5
+ ],
6
+ "attention_dropout": 0.0,
7
+ "bos_token_id": 1,
8
+ "eos_token_id": 32000,
9
+ "hidden_act": "silu",
10
+ "hidden_size": 4096,
11
+ "initializer_range": 0.02,
12
+ "intermediate_size": 14336,
13
+ "max_position_embeddings": 32768,
14
+ "model_type": "mistral",
15
+ "num_attention_heads": 32,
16
+ "num_hidden_layers": 32,
17
+ "num_key_value_heads": 8,
18
+ "rms_norm_eps": 1e-05,
19
+ "rope_theta": 10000.0,
20
+ "sliding_window": 4096,
21
+ "tie_word_embeddings": false,
22
+ "torch_dtype": "bfloat16",
23
+ "transformers_version": "4.38.2",
24
+ "use_cache": false,
25
+ "vocab_size": 32032,
26
+ "quantization_config": {
27
+ "quant_method": "exl2",
28
+ "version": "0.0.15",
29
+ "bits": 3.5,
30
+ "head_bits": 6,
31
+ "calibration": {
32
+ "rows": 100,
33
+ "length": 2048,
34
+ "dataset": "(default)"
35
+ }
36
+ }
37
+ }
generation_config.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "_from_model_config": true,
3
+ "bos_token_id": 1,
4
+ "do_sample": true,
5
+ "eos_token_id": 32000,
6
+ "transformers_version": "4.38.2"
7
+ }
model.safetensors.index.json ADDED
@@ -0,0 +1,298 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metadata": {
3
+ "total_size": 14483988480
4
+ },
5
+ "weight_map": {
6
+ "lm_head.weight": "model-00004-of-00004.safetensors",
7
+ "model.embed_tokens.weight": "model-00001-of-00004.safetensors",
8
+ "model.layers.0.input_layernorm.weight": "model-00001-of-00004.safetensors",
9
+ "model.layers.0.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
10
+ "model.layers.0.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
11
+ "model.layers.0.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
12
+ "model.layers.0.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
13
+ "model.layers.0.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
14
+ "model.layers.0.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
15
+ "model.layers.0.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
16
+ "model.layers.0.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
17
+ "model.layers.1.input_layernorm.weight": "model-00001-of-00004.safetensors",
18
+ "model.layers.1.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
19
+ "model.layers.1.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
20
+ "model.layers.1.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
21
+ "model.layers.1.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
22
+ "model.layers.1.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
23
+ "model.layers.1.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
24
+ "model.layers.1.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
25
+ "model.layers.1.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
26
+ "model.layers.10.input_layernorm.weight": "model-00002-of-00004.safetensors",
27
+ "model.layers.10.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
28
+ "model.layers.10.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
29
+ "model.layers.10.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
30
+ "model.layers.10.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
31
+ "model.layers.10.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
32
+ "model.layers.10.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
33
+ "model.layers.10.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
34
+ "model.layers.10.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
35
+ "model.layers.11.input_layernorm.weight": "model-00002-of-00004.safetensors",
36
+ "model.layers.11.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
37
+ "model.layers.11.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
38
+ "model.layers.11.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
39
+ "model.layers.11.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
40
+ "model.layers.11.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
41
+ "model.layers.11.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
42
+ "model.layers.11.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
43
+ "model.layers.11.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
44
+ "model.layers.12.input_layernorm.weight": "model-00002-of-00004.safetensors",
45
+ "model.layers.12.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
46
+ "model.layers.12.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
47
+ "model.layers.12.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
48
+ "model.layers.12.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
49
+ "model.layers.12.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
50
+ "model.layers.12.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
51
+ "model.layers.12.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
52
+ "model.layers.12.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
53
+ "model.layers.13.input_layernorm.weight": "model-00002-of-00004.safetensors",
54
+ "model.layers.13.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
55
+ "model.layers.13.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
56
+ "model.layers.13.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
57
+ "model.layers.13.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
58
+ "model.layers.13.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
59
+ "model.layers.13.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
60
+ "model.layers.13.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
61
+ "model.layers.13.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
62
+ "model.layers.14.input_layernorm.weight": "model-00002-of-00004.safetensors",
63
+ "model.layers.14.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
64
+ "model.layers.14.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
65
+ "model.layers.14.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
66
+ "model.layers.14.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
67
+ "model.layers.14.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
68
+ "model.layers.14.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
69
+ "model.layers.14.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
70
+ "model.layers.14.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
71
+ "model.layers.15.input_layernorm.weight": "model-00002-of-00004.safetensors",
72
+ "model.layers.15.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
73
+ "model.layers.15.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
74
+ "model.layers.15.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
75
+ "model.layers.15.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
76
+ "model.layers.15.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
77
+ "model.layers.15.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
78
+ "model.layers.15.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
79
+ "model.layers.15.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
80
+ "model.layers.16.input_layernorm.weight": "model-00002-of-00004.safetensors",
81
+ "model.layers.16.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
82
+ "model.layers.16.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
83
+ "model.layers.16.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
84
+ "model.layers.16.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
85
+ "model.layers.16.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
86
+ "model.layers.16.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
87
+ "model.layers.16.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
88
+ "model.layers.16.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
89
+ "model.layers.17.input_layernorm.weight": "model-00003-of-00004.safetensors",
90
+ "model.layers.17.mlp.down_proj.weight": "model-00003-of-00004.safetensors",
91
+ "model.layers.17.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
92
+ "model.layers.17.mlp.up_proj.weight": "model-00003-of-00004.safetensors",
93
+ "model.layers.17.post_attention_layernorm.weight": "model-00003-of-00004.safetensors",
94
+ "model.layers.17.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
95
+ "model.layers.17.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
96
+ "model.layers.17.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
97
+ "model.layers.17.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
98
+ "model.layers.18.input_layernorm.weight": "model-00003-of-00004.safetensors",
99
+ "model.layers.18.mlp.down_proj.weight": "model-00003-of-00004.safetensors",
100
+ "model.layers.18.mlp.gate_proj.weight": "model-00003-of-00004.safetensors",
101
+ "model.layers.18.mlp.up_proj.weight": "model-00003-of-00004.safetensors",
102
+ "model.layers.18.post_attention_layernorm.weight": "model-00003-of-00004.safetensors",
103
+ "model.layers.18.self_attn.k_proj.weight": "model-00003-of-00004.safetensors",
104
+ "model.layers.18.self_attn.o_proj.weight": "model-00003-of-00004.safetensors",
105
+ "model.layers.18.self_attn.q_proj.weight": "model-00003-of-00004.safetensors",
106
+ "model.layers.18.self_attn.v_proj.weight": "model-00003-of-00004.safetensors",
107
+ "model.layers.19.input_layernorm.weight": "model-00003-of-00004.safetensors",
108
+ "model.layers.19.mlp.down_proj.weight": "model-00003-of-00004.safetensors",
109
+ "model.layers.19.mlp.gate_proj.weight": "model-00003-of-00004.safetensors",
110
+ "model.layers.19.mlp.up_proj.weight": "model-00003-of-00004.safetensors",
111
+ "model.layers.19.post_attention_layernorm.weight": "model-00003-of-00004.safetensors",
112
+ "model.layers.19.self_attn.k_proj.weight": "model-00003-of-00004.safetensors",
113
+ "model.layers.19.self_attn.o_proj.weight": "model-00003-of-00004.safetensors",
114
+ "model.layers.19.self_attn.q_proj.weight": "model-00003-of-00004.safetensors",
115
+ "model.layers.19.self_attn.v_proj.weight": "model-00003-of-00004.safetensors",
116
+ "model.layers.2.input_layernorm.weight": "model-00001-of-00004.safetensors",
117
+ "model.layers.2.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
118
+ "model.layers.2.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
119
+ "model.layers.2.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
120
+ "model.layers.2.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
121
+ "model.layers.2.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
122
+ "model.layers.2.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
123
+ "model.layers.2.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
124
+ "model.layers.2.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
125
+ "model.layers.20.input_layernorm.weight": "model-00003-of-00004.safetensors",
126
+ "model.layers.20.mlp.down_proj.weight": "model-00003-of-00004.safetensors",
127
+ "model.layers.20.mlp.gate_proj.weight": "model-00003-of-00004.safetensors",
128
+ "model.layers.20.mlp.up_proj.weight": "model-00003-of-00004.safetensors",
129
+ "model.layers.20.post_attention_layernorm.weight": "model-00003-of-00004.safetensors",
130
+ "model.layers.20.self_attn.k_proj.weight": "model-00003-of-00004.safetensors",
131
+ "model.layers.20.self_attn.o_proj.weight": "model-00003-of-00004.safetensors",
132
+ "model.layers.20.self_attn.q_proj.weight": "model-00003-of-00004.safetensors",
133
+ "model.layers.20.self_attn.v_proj.weight": "model-00003-of-00004.safetensors",
134
+ "model.layers.21.input_layernorm.weight": "model-00003-of-00004.safetensors",
135
+ "model.layers.21.mlp.down_proj.weight": "model-00003-of-00004.safetensors",
136
+ "model.layers.21.mlp.gate_proj.weight": "model-00003-of-00004.safetensors",
137
+ "model.layers.21.mlp.up_proj.weight": "model-00003-of-00004.safetensors",
138
+ "model.layers.21.post_attention_layernorm.weight": "model-00003-of-00004.safetensors",
139
+ "model.layers.21.self_attn.k_proj.weight": "model-00003-of-00004.safetensors",
140
+ "model.layers.21.self_attn.o_proj.weight": "model-00003-of-00004.safetensors",
141
+ "model.layers.21.self_attn.q_proj.weight": "model-00003-of-00004.safetensors",
142
+ "model.layers.21.self_attn.v_proj.weight": "model-00003-of-00004.safetensors",
143
+ "model.layers.22.input_layernorm.weight": "model-00003-of-00004.safetensors",
144
+ "model.layers.22.mlp.down_proj.weight": "model-00003-of-00004.safetensors",
145
+ "model.layers.22.mlp.gate_proj.weight": "model-00003-of-00004.safetensors",
146
+ "model.layers.22.mlp.up_proj.weight": "model-00003-of-00004.safetensors",
147
+ "model.layers.22.post_attention_layernorm.weight": "model-00003-of-00004.safetensors",
148
+ "model.layers.22.self_attn.k_proj.weight": "model-00003-of-00004.safetensors",
149
+ "model.layers.22.self_attn.o_proj.weight": "model-00003-of-00004.safetensors",
150
+ "model.layers.22.self_attn.q_proj.weight": "model-00003-of-00004.safetensors",
151
+ "model.layers.22.self_attn.v_proj.weight": "model-00003-of-00004.safetensors",
152
+ "model.layers.23.input_layernorm.weight": "model-00003-of-00004.safetensors",
153
+ "model.layers.23.mlp.down_proj.weight": "model-00003-of-00004.safetensors",
154
+ "model.layers.23.mlp.gate_proj.weight": "model-00003-of-00004.safetensors",
155
+ "model.layers.23.mlp.up_proj.weight": "model-00003-of-00004.safetensors",
156
+ "model.layers.23.post_attention_layernorm.weight": "model-00003-of-00004.safetensors",
157
+ "model.layers.23.self_attn.k_proj.weight": "model-00003-of-00004.safetensors",
158
+ "model.layers.23.self_attn.o_proj.weight": "model-00003-of-00004.safetensors",
159
+ "model.layers.23.self_attn.q_proj.weight": "model-00003-of-00004.safetensors",
160
+ "model.layers.23.self_attn.v_proj.weight": "model-00003-of-00004.safetensors",
161
+ "model.layers.24.input_layernorm.weight": "model-00003-of-00004.safetensors",
162
+ "model.layers.24.mlp.down_proj.weight": "model-00003-of-00004.safetensors",
163
+ "model.layers.24.mlp.gate_proj.weight": "model-00003-of-00004.safetensors",
164
+ "model.layers.24.mlp.up_proj.weight": "model-00003-of-00004.safetensors",
165
+ "model.layers.24.post_attention_layernorm.weight": "model-00003-of-00004.safetensors",
166
+ "model.layers.24.self_attn.k_proj.weight": "model-00003-of-00004.safetensors",
167
+ "model.layers.24.self_attn.o_proj.weight": "model-00003-of-00004.safetensors",
168
+ "model.layers.24.self_attn.q_proj.weight": "model-00003-of-00004.safetensors",
169
+ "model.layers.24.self_attn.v_proj.weight": "model-00003-of-00004.safetensors",
170
+ "model.layers.25.input_layernorm.weight": "model-00003-of-00004.safetensors",
171
+ "model.layers.25.mlp.down_proj.weight": "model-00003-of-00004.safetensors",
172
+ "model.layers.25.mlp.gate_proj.weight": "model-00003-of-00004.safetensors",
173
+ "model.layers.25.mlp.up_proj.weight": "model-00003-of-00004.safetensors",
174
+ "model.layers.25.post_attention_layernorm.weight": "model-00003-of-00004.safetensors",
175
+ "model.layers.25.self_attn.k_proj.weight": "model-00003-of-00004.safetensors",
176
+ "model.layers.25.self_attn.o_proj.weight": "model-00003-of-00004.safetensors",
177
+ "model.layers.25.self_attn.q_proj.weight": "model-00003-of-00004.safetensors",
178
+ "model.layers.25.self_attn.v_proj.weight": "model-00003-of-00004.safetensors",
179
+ "model.layers.26.input_layernorm.weight": "model-00004-of-00004.safetensors",
180
+ "model.layers.26.mlp.down_proj.weight": "model-00004-of-00004.safetensors",
181
+ "model.layers.26.mlp.gate_proj.weight": "model-00003-of-00004.safetensors",
182
+ "model.layers.26.mlp.up_proj.weight": "model-00004-of-00004.safetensors",
183
+ "model.layers.26.post_attention_layernorm.weight": "model-00004-of-00004.safetensors",
184
+ "model.layers.26.self_attn.k_proj.weight": "model-00003-of-00004.safetensors",
185
+ "model.layers.26.self_attn.o_proj.weight": "model-00003-of-00004.safetensors",
186
+ "model.layers.26.self_attn.q_proj.weight": "model-00003-of-00004.safetensors",
187
+ "model.layers.26.self_attn.v_proj.weight": "model-00003-of-00004.safetensors",
188
+ "model.layers.27.input_layernorm.weight": "model-00004-of-00004.safetensors",
189
+ "model.layers.27.mlp.down_proj.weight": "model-00004-of-00004.safetensors",
190
+ "model.layers.27.mlp.gate_proj.weight": "model-00004-of-00004.safetensors",
191
+ "model.layers.27.mlp.up_proj.weight": "model-00004-of-00004.safetensors",
192
+ "model.layers.27.post_attention_layernorm.weight": "model-00004-of-00004.safetensors",
193
+ "model.layers.27.self_attn.k_proj.weight": "model-00004-of-00004.safetensors",
194
+ "model.layers.27.self_attn.o_proj.weight": "model-00004-of-00004.safetensors",
195
+ "model.layers.27.self_attn.q_proj.weight": "model-00004-of-00004.safetensors",
196
+ "model.layers.27.self_attn.v_proj.weight": "model-00004-of-00004.safetensors",
197
+ "model.layers.28.input_layernorm.weight": "model-00004-of-00004.safetensors",
198
+ "model.layers.28.mlp.down_proj.weight": "model-00004-of-00004.safetensors",
199
+ "model.layers.28.mlp.gate_proj.weight": "model-00004-of-00004.safetensors",
200
+ "model.layers.28.mlp.up_proj.weight": "model-00004-of-00004.safetensors",
201
+ "model.layers.28.post_attention_layernorm.weight": "model-00004-of-00004.safetensors",
202
+ "model.layers.28.self_attn.k_proj.weight": "model-00004-of-00004.safetensors",
203
+ "model.layers.28.self_attn.o_proj.weight": "model-00004-of-00004.safetensors",
204
+ "model.layers.28.self_attn.q_proj.weight": "model-00004-of-00004.safetensors",
205
+ "model.layers.28.self_attn.v_proj.weight": "model-00004-of-00004.safetensors",
206
+ "model.layers.29.input_layernorm.weight": "model-00004-of-00004.safetensors",
207
+ "model.layers.29.mlp.down_proj.weight": "model-00004-of-00004.safetensors",
208
+ "model.layers.29.mlp.gate_proj.weight": "model-00004-of-00004.safetensors",
209
+ "model.layers.29.mlp.up_proj.weight": "model-00004-of-00004.safetensors",
210
+ "model.layers.29.post_attention_layernorm.weight": "model-00004-of-00004.safetensors",
211
+ "model.layers.29.self_attn.k_proj.weight": "model-00004-of-00004.safetensors",
212
+ "model.layers.29.self_attn.o_proj.weight": "model-00004-of-00004.safetensors",
213
+ "model.layers.29.self_attn.q_proj.weight": "model-00004-of-00004.safetensors",
214
+ "model.layers.29.self_attn.v_proj.weight": "model-00004-of-00004.safetensors",
215
+ "model.layers.3.input_layernorm.weight": "model-00001-of-00004.safetensors",
216
+ "model.layers.3.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
217
+ "model.layers.3.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
218
+ "model.layers.3.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
219
+ "model.layers.3.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
220
+ "model.layers.3.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
221
+ "model.layers.3.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
222
+ "model.layers.3.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
223
+ "model.layers.3.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
224
+ "model.layers.30.input_layernorm.weight": "model-00004-of-00004.safetensors",
225
+ "model.layers.30.mlp.down_proj.weight": "model-00004-of-00004.safetensors",
226
+ "model.layers.30.mlp.gate_proj.weight": "model-00004-of-00004.safetensors",
227
+ "model.layers.30.mlp.up_proj.weight": "model-00004-of-00004.safetensors",
228
+ "model.layers.30.post_attention_layernorm.weight": "model-00004-of-00004.safetensors",
229
+ "model.layers.30.self_attn.k_proj.weight": "model-00004-of-00004.safetensors",
230
+ "model.layers.30.self_attn.o_proj.weight": "model-00004-of-00004.safetensors",
231
+ "model.layers.30.self_attn.q_proj.weight": "model-00004-of-00004.safetensors",
232
+ "model.layers.30.self_attn.v_proj.weight": "model-00004-of-00004.safetensors",
233
+ "model.layers.31.input_layernorm.weight": "model-00004-of-00004.safetensors",
234
+ "model.layers.31.mlp.down_proj.weight": "model-00004-of-00004.safetensors",
235
+ "model.layers.31.mlp.gate_proj.weight": "model-00004-of-00004.safetensors",
236
+ "model.layers.31.mlp.up_proj.weight": "model-00004-of-00004.safetensors",
237
+ "model.layers.31.post_attention_layernorm.weight": "model-00004-of-00004.safetensors",
238
+ "model.layers.31.self_attn.k_proj.weight": "model-00004-of-00004.safetensors",
239
+ "model.layers.31.self_attn.o_proj.weight": "model-00004-of-00004.safetensors",
240
+ "model.layers.31.self_attn.q_proj.weight": "model-00004-of-00004.safetensors",
241
+ "model.layers.31.self_attn.v_proj.weight": "model-00004-of-00004.safetensors",
242
+ "model.layers.4.input_layernorm.weight": "model-00001-of-00004.safetensors",
243
+ "model.layers.4.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
244
+ "model.layers.4.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
245
+ "model.layers.4.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
246
+ "model.layers.4.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
247
+ "model.layers.4.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
248
+ "model.layers.4.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
249
+ "model.layers.4.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
250
+ "model.layers.4.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
251
+ "model.layers.5.input_layernorm.weight": "model-00001-of-00004.safetensors",
252
+ "model.layers.5.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
253
+ "model.layers.5.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
254
+ "model.layers.5.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
255
+ "model.layers.5.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
256
+ "model.layers.5.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
257
+ "model.layers.5.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
258
+ "model.layers.5.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
259
+ "model.layers.5.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
260
+ "model.layers.6.input_layernorm.weight": "model-00001-of-00004.safetensors",
261
+ "model.layers.6.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
262
+ "model.layers.6.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
263
+ "model.layers.6.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
264
+ "model.layers.6.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
265
+ "model.layers.6.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
266
+ "model.layers.6.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
267
+ "model.layers.6.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
268
+ "model.layers.6.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
269
+ "model.layers.7.input_layernorm.weight": "model-00001-of-00004.safetensors",
270
+ "model.layers.7.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
271
+ "model.layers.7.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
272
+ "model.layers.7.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
273
+ "model.layers.7.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
274
+ "model.layers.7.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
275
+ "model.layers.7.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
276
+ "model.layers.7.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
277
+ "model.layers.7.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
278
+ "model.layers.8.input_layernorm.weight": "model-00002-of-00004.safetensors",
279
+ "model.layers.8.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
280
+ "model.layers.8.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
281
+ "model.layers.8.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
282
+ "model.layers.8.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
283
+ "model.layers.8.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
284
+ "model.layers.8.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
285
+ "model.layers.8.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
286
+ "model.layers.8.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
287
+ "model.layers.9.input_layernorm.weight": "model-00002-of-00004.safetensors",
288
+ "model.layers.9.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
289
+ "model.layers.9.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
290
+ "model.layers.9.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
291
+ "model.layers.9.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
292
+ "model.layers.9.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
293
+ "model.layers.9.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
294
+ "model.layers.9.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
295
+ "model.layers.9.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
296
+ "model.norm.weight": "model-00004-of-00004.safetensors"
297
+ }
298
+ }
output.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:e5e1a1df904bc83e9c495eac27c5efb4598144a086a5dd1f36b368fe836a62ee
3
+ size 3420152392
special_tokens_map.json ADDED
@@ -0,0 +1,30 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<s>",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "eos_token": {
10
+ "content": "<|im_end|>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "pad_token": {
17
+ "content": "</s>",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ },
23
+ "unk_token": {
24
+ "content": "<unk>",
25
+ "lstrip": false,
26
+ "normalized": false,
27
+ "rstrip": false,
28
+ "single_word": false
29
+ }
30
+ }
tokenizer.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:dadfd56d766715c61d2ef780a525ab43b8e6da4de6865bda3d95fdef5e134055
3
+ size 493443
tokenizer_config.json ADDED
@@ -0,0 +1,61 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_bos_token": true,
3
+ "add_eos_token": false,
4
+ "add_prefix_space": true,
5
+ "added_tokens_decoder": {
6
+ "0": {
7
+ "content": "<unk>",
8
+ "lstrip": false,
9
+ "normalized": false,
10
+ "rstrip": false,
11
+ "single_word": false,
12
+ "special": true
13
+ },
14
+ "1": {
15
+ "content": "<s>",
16
+ "lstrip": false,
17
+ "normalized": false,
18
+ "rstrip": false,
19
+ "single_word": false,
20
+ "special": true
21
+ },
22
+ "2": {
23
+ "content": "</s>",
24
+ "lstrip": false,
25
+ "normalized": false,
26
+ "rstrip": false,
27
+ "single_word": false,
28
+ "special": true
29
+ },
30
+ "32000": {
31
+ "content": "<|im_end|>",
32
+ "lstrip": false,
33
+ "normalized": false,
34
+ "rstrip": false,
35
+ "single_word": false,
36
+ "special": true
37
+ },
38
+ "32001": {
39
+ "content": "<|im_start|>",
40
+ "lstrip": false,
41
+ "normalized": false,
42
+ "rstrip": false,
43
+ "single_word": false,
44
+ "special": false
45
+ }
46
+ },
47
+ "additional_special_tokens": [],
48
+ "bos_token": "<s>",
49
+ "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
50
+ "clean_up_tokenization_spaces": false,
51
+ "eos_token": "<|im_end|>",
52
+ "legacy": true,
53
+ "model_max_length": 1000000000000000019884624838656,
54
+ "pad_token": "</s>",
55
+ "sp_model_kwargs": {},
56
+ "spaces_between_special_tokens": false,
57
+ "tokenizer_class": "LlamaTokenizer",
58
+ "unk_token": "<unk>",
59
+ "use_default_system_prompt": false,
60
+ "use_fast": true
61
+ }