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---
base_model: Qwen/Qwen2.5-14B-Instruct
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-14B-Instruct-AWQ/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- chat
---
# Qwen2.5-14B-Instruct-AWQ
## Introduction
Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
- Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains.
- Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots.
- **Long-context Support** up to 128K tokens and can generate up to 8K tokens.
- **Multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
**This repo contains the AWQ-quantized 4-bit instruction-tuned 72B Qwen2.5 model**, which has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
- Number of Parameters: 14.7B
- Number of Paramaters (Non-Embedding): 13.1B
- Number of Layers: 48
- Number of Attention Heads (GQA): 40 for Q and 8 for KV
- Context Length: Full 131,072 tokens and generation 8192 tokens
- Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts.
- Quantization: AWQ 4-bit
For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Requirements
The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.37.0`, you will encounter the following error:
```
KeyError: 'qwen2'
```
Also check out our [AWQ documentation](https://qwen.readthedocs.io/en/latest/quantization/awq.html) for more usage guide.
## Quickstart
Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen2.5-14B-Instruct-AWQ"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
### Processing Long Texts
The current `config.json` is set for context length up to 32,768 tokens.
To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.
For supported frameworks, you could add the following to `config.json` to enable YaRN:
```json
{
...,
"rope_scaling": {
"factor": 4.0,
"original_max_position_embeddings": 32768,
"type": "yarn"
}
}
```
For deployment, we recommend using vLLM.
Please refer to our [Documentation](https://qwen.readthedocs.io/en/latest/deployment/vllm.html) for usage if you are not familar with vLLM.
Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts**.
We advise adding the `rope_scaling` configuration only when processing long contexts is required.
## Evaluation & Performance
Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/).
For quantized models, the benchmark results against the original bfloat16 models can be found [here](https://qwen.readthedocs.io/en/latest/benchmark/quantization_benchmark.html)
For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
## Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen2.5,
title = {Qwen2.5: A Party of Foundation Models},
url = {https://qwenlm.github.io/blog/qwen2.5/},
author = {Qwen Team},
month = {September},
year = {2024}
}
@article{qwen2,
title={Qwen2 Technical Report},
author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
journal={arXiv preprint arXiv:2407.10671},
year={2024}
}
```

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{
"architectures": [
"Qwen2ForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 151643,
"eos_token_id": 151645,
"hidden_act": "silu",
"hidden_size": 5120,
"initializer_range": 0.02,
"intermediate_size": 13824,
"max_position_embeddings": 32768,
"max_window_layers": 70,
"model_type": "qwen2",
"num_attention_heads": 40,
"num_hidden_layers": 48,
"num_key_value_heads": 8,
"quantization_config": {
"bits": 4,
"group_size": 128,
"modules_to_not_convert": null,
"quant_method": "awq",
"version": "gemm",
"zero_point": true
},
"rms_norm_eps": 1e-06,
"rope_theta": 1000000.0,
"sliding_window": 131072,
"tie_word_embeddings": false,
"torch_dtype": "float16",
"transformers_version": "4.41.1",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 152064
}

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{
"bos_token_id": 151643,
"do_sample": true,
"eos_token_id": [
151645,
151643
],
"pad_token_id": 151643,
"repetition_penalty": 1.05,
"temperature": 0.7,
"top_k": 20,
"top_p": 0.8,
"transformers_version": "4.41.1"
}

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import gc, torch
from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
import re
import pandas as pd
from collections import Counter
# Free a model already on the GPU, so re-running this cell doesn't stack a second copy and run out of
# memory. (If you still hit "out of memory", do Runtime -> Restart session and run this cell just once.)
model = tok = None
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_peak_memory_stats()
MODEL_ID = "."
MAX_NEW_TOKENS = 2048
generation_config = dict(
do_sample=True,
temperature=0.3,
top_p=0.9,
top_k=40,
repetition_penalty=1.05,
)
tok = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID, torch_dtype=torch.float16, device_map="auto",
).eval()
print("loaded", MODEL_ID, "| VRAM", round(torch.cuda.max_memory_allocated() / 1e9, 1), "GB")
df = pd.read_csv("/tmp/data/test.csv", dtype=str).fillna("")
SOLVER_SYSTEM = """
You are an expert International Linguistics Olympiad (IOL) solver.
Your task is to infer the hidden linguistic rules ONLY from the provided examples.
REQUIRED WORKFLOW:
1. SCRATCHPAD: List all recurring units (morphemes, words, or sounds) and their meanings.
2. RULE VERIFICATION: Write down rules for combining these units. Test against examples.
3. FINAL DERIVATION: Step-by-step derivation for each query item.
IMPORTANT RULES:
- Never rely on outside linguistic knowledge.
- If the task is 'match_letters', the FINAL ANSWERS must be ONLY the letter (e.g., A, B, C) that corresponds to each query item, one per line. Do NOT output the word itself.
- If the target language is phonetic (uses brackets [] or special symbols), keep that notation exactly.
- ABSOLUTELY NO ENGLISH in the FINAL ANSWERS section unless the target language is English.
- Output exactly one answer per query item.
Output format:
FINAL ANSWERS:
[Answer 1]
[Answer 2]
... (one per line)
"""
VALIDATOR_SYSTEM = """
You are an expert IOL solution validator.
Check if the FINAL ANSWERS match the expected format of the query:
- For 'match_letters', are they ONLY single letters (A, B, C...)? If they are words, it is INVALID.
- For 'translation' or 'fill_blanks', are they in the target language (not English)? If there is English, it is INVALID.
- Is the number of answers correct?
Output ONLY 'VALID' or 'INVALID' followed by specific contradictions.
"""
CORRECTOR_SYSTEM = """
You are correcting an IOL solution.
- If the task is 'match_letters', replace words with the corresponding labels (A, B, C).
- Ensure the FINAL ANSWERS contain ONLY the target language forms.
- Remove all English translations, explanations, or labels like 'Item 1:'.
Output exactly:
FINAL ANSWERS:
followed by one answer per line.
"""
def parse_answers(text):
m = list(re.finditer(r"(?im)^\s*FINAL ANSWERS\s*:?\s*$", text))
if m:
text = text[m[-1].end():]
answers = []
for line in text.splitlines():
line = line.strip()
if not line:
continue
# Clean formatting
line = re.sub(r"^\d+[.)]\s*", "", line)
line = re.sub(r"^[-*•]\s*", "", line)
line = line.strip("'\" ")
# Heuristic to filter out leaked reasoning:
# 1. Skip lines that contain markdown bolding or italics
if '*' in line or '_' in line:
continue
# 2. Skip lines that look like full sentences (too many spaces)
# unless it's a translation task where the target is a sentence.
if line.count(' ') > 5 and len(line) > 50:
continue
answers.append(line)
return answers
def constraint_check(problem, answers):
errors = []
if len(answers) == 0:
errors.append("No answers generated.")
# No empty answers
for i,a in enumerate(answers):
if len(a.strip()) == 0:
errors.append(f"Answer {i+1} is empty.")
# Remove duplicate consecutive answers
for i in range(1,len(answers)):
if answers[i] == answers[i-1]:
errors.append("Duplicate consecutive answers.")
# Very long outputs
for a in answers:
if len(a) > 120:
errors.append("Answer too long.")
return errors
def generate(system_prompt, user_prompt):
messages = [
{"role":"system","content":system_prompt},
{"role":"user","content":user_prompt},
]
text = tok.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = tok(
text,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=MAX_NEW_TOKENS,
do_sample=True,
# Lowered temperature for more stable linguistic reasoning
temperature=0.1,
top_p=0.95,
top_k=50,
repetition_penalty=1.05,
)
output = tok.decode(
outputs[0][inputs.input_ids.shape[1]:],
skip_special_tokens=True
)
return output
def self_consistency(problem, n=3):
candidates_with_raw = [] # Store (parsed_answers_tuple, raw_text_string)
for _ in range(n):
raw_out_text = generate(
SOLVER_SYSTEM,
problem
)
parsed_ans = tuple(parse_answers(raw_out_text))
candidates_with_raw.append((parsed_ans, raw_out_text))
# Count occurrences of parsed answers
parsed_ans_counts = Counter(item[0] for item in candidates_with_raw)
best_parsed_ans = parsed_ans_counts.most_common(1)[0][0]
# Find the raw text that produced the best_parsed_ans (take the first one if multiple)
best_raw_text = None
for parsed_ans, raw_text_candidate in candidates_with_raw:
if parsed_ans == best_parsed_ans:
best_raw_text = raw_text_candidate
break
return list(best_parsed_ans), best_raw_text
def validate(problem, answers):
prompt = f"""
Problem
{problem}
Candidate solution
FINAL ANSWERS:
{chr(10).join(answers)}
"""
result = generate(
VALIDATOR_SYSTEM,
prompt
)
return result
def correct(problem, answers, validator_output):
prompt = f"""
Problem
{problem}
Previous answer
FINAL ANSWERS:
{chr(10).join(answers)}
Validation
{validator_output}
"""
result = generate(
CORRECTOR_SYSTEM,
prompt
)
return parse_answers(result)
def solve(problem):
answers, raw_output_for_best_ans = self_consistency(
problem,
n=1
)
raw_text = raw_output_for_best_ans
for _ in range(3):
validator = validate(
problem,
answers
)
constraints = constraint_check(
problem,
answers
)
if validator.strip() == "VALID" and len(constraints) == 0:
return answers, raw_text
answers = correct(
problem,
answers,
validator + "\n" + "\n".join(constraints)
)
return answers, raw_text
results = []
for i, r in df.iterrows():
print(f"\n{'=' * 72}\nPROBLEM {i + 1}/{len(df)} -- {r['task_type']}\n{'=' * 72}", flush=True)
problem_text = f"{r['context'].strip()}\n\n{r['query'].strip()}"
answers, raw_text = solve(problem_text)
results.append({"query": r["query"], "raw": raw_text, "pred": answers})
print(f"\n--> parsed {len(answers)} answers", flush=True)
SUMMARIZE = (
"Summarize the following reasoning into a few short bullet points: the rule or pattern found "
"in the data and the key evidence for the answer. Be concise and structured -- do not repeat "
"the full reasoning."
)
for i, res in enumerate(results):
print(f"\n{'=' * 72}\nPROBLEM {i + 1} -- explanation\n{'=' * 72}", flush=True)
messages = [
{"role": "system", "content": SUMMARIZE},
{"role": "user", "content": res["raw"]},
]
enc = tok.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt", return_dict=True,
).to(model.device)
streamer = TextStreamer(tok, skip_prompt=True, skip_special_tokens=True)
with torch.no_grad():
out = model.generate(**enc, max_new_tokens=400, do_sample=False, streamer=streamer)
res["explanation"] = tok.decode(out[0][enc["input_ids"].shape[-1]:], skip_special_tokens=True).strip()
print()
submission = pd.DataFrame([
{
"id": i + 1,
"pred": res["pred"],
"explanation": res["explanation"],
}
for i, res in enumerate(results)
])
submission.to_csv("submission.csv", index=False)
print(submission.head())
print(f"Saved {len(submission)} predictions to submission.csv")

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{
"add_bos_token": false,
"add_prefix_space": false,
"added_tokens_decoder": {
"151643": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151644": {
"content": "<|im_start|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151645": {
"content": "<|im_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151646": {
"content": "<|object_ref_start|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151647": {
"content": "<|object_ref_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151648": {
"content": "<|box_start|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151649": {
"content": "<|box_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151650": {
"content": "<|quad_start|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151651": {
"content": "<|quad_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151652": {
"content": "<|vision_start|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151653": {
"content": "<|vision_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151654": {
"content": "<|vision_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151655": {
"content": "<|image_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151656": {
"content": "<|video_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151657": {
"content": "<tool_call>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151658": {
"content": "</tool_call>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151659": {
"content": "<|fim_prefix|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151660": {
"content": "<|fim_middle|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151661": {
"content": "<|fim_suffix|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151662": {
"content": "<|fim_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151663": {
"content": "<|repo_name|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151664": {
"content": "<|file_sep|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
}
},
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
"<|quad_end|>",
"<|vision_start|>",
"<|vision_end|>",
"<|vision_pad|>",
"<|image_pad|>",
"<|video_pad|>"
],
"bos_token": null,
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"model_max_length": 131072,
"pad_token": "<|endoftext|>",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

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