Text Generation
Transformers
Safetensors
qwen2
Generated from Trainer
gkd
hf_jobs
qwen-distill
trackio
trl
trl-autoresearch
conversational
text-generation-inference
Instructions to use cmpatino/qwen-distill-r1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cmpatino/qwen-distill-r1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cmpatino/qwen-distill-r1") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("cmpatino/qwen-distill-r1") model = AutoModelForCausalLM.from_pretrained("cmpatino/qwen-distill-r1", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use cmpatino/qwen-distill-r1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cmpatino/qwen-distill-r1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cmpatino/qwen-distill-r1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cmpatino/qwen-distill-r1
- SGLang
How to use cmpatino/qwen-distill-r1 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "cmpatino/qwen-distill-r1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cmpatino/qwen-distill-r1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "cmpatino/qwen-distill-r1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cmpatino/qwen-distill-r1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use cmpatino/qwen-distill-r1 with Docker Model Runner:
docker model run hf.co/cmpatino/qwen-distill-r1
Training in progress, step 200
Browse files- .gitattributes +1 -0
- README.md +70 -0
- chat_template.jinja +6 -0
- config.json +57 -0
- generation_config.json +14 -0
- model.safetensors +3 -0
- tokenizer.json +3 -0
- tokenizer_config.json +20 -0
- training_args.bin +3 -0
.gitattributes
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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base_model: Qwen/Qwen2-0.5B-Instruct
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library_name: transformers
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model_name: qwen-distill-r1
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tags:
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- generated_from_trainer
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- hf_jobs
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- gkd
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- trl
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licence: license
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---
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# Model Card for qwen-distill-r1
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This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct).
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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```python
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from transformers import pipeline
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question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
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generator = pipeline("text-generation", model="cmpatino/qwen-distill-r1", device="cuda")
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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## Training procedure
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This model was trained with GKD, a method introduced in [On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes](https://huggingface.co/papers/2306.13649).
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### Framework versions
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- TRL: 1.9.2
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- Transformers: 5.14.1
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- Pytorch: 2.13.0
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- Datasets: 5.0.1
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- Tokenizers: 0.22.2
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## Citations
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Cite GKD as:
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```bibtex
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@inproceedings{agarwal2024on-policy,
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title = {{On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes}},
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author = {Rishabh Agarwal and Nino Vieillard and Yongchao Zhou and Piotr Stanczyk and Sabela Ramos Garea and Matthieu Geist and Olivier Bachem},
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year = 2024,
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booktitle = {The Twelfth International Conference on Learning Representations, {ICLR} 2024, Vienna, Austria, May 7-11, 2024},
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publisher = {OpenReview.net},
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url = {https://openreview.net/forum?id=3zKtaqxLhW},
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}
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```
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Cite TRL as:
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```bibtex
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@software{vonwerra2020trl,
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title = {{TRL: Transformers Reinforcement Learning}},
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author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
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license = {Apache-2.0},
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url = {https://github.com/huggingface/trl},
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year = {2020}
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}
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```
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chat_template.jinja
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{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
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You are a helpful assistant.<|im_end|>
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' }}{% endif %}{{'<|im_start|>' + message['role'] + '
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' + message['content'] + '<|im_end|>' + '
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'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
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' }}{% endif %}
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": null,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 896,
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"initializer_range": 0.02,
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"intermediate_size": 4864,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 24,
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"model_type": "qwen2",
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"num_attention_heads": 14,
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"num_hidden_layers": 24,
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"num_key_value_heads": 2,
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"pad_token_id": 151643,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000.0,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.14.1",
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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generation_config.json
ADDED
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{
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"repetition_penalty": 1.1,
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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| 12 |
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"transformers_version": "5.14.1",
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"use_cache": false
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:2e701cb8f4f80ff5675e92c2c9e9a8d1c765e682c26151340b26e8aafd78a2ab
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size 988097824
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:48f722bc04c884e2fe1525fdcd85a1293a8499b6e620c1ac7c083c49632305fb
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size 11418262
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": null,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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| 7 |
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"errors": "replace",
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| 8 |
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"extra_special_tokens": [
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| 9 |
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"<|im_start|>",
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"<|im_end|>"
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],
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"is_local": false,
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"local_files_only": false,
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| 14 |
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"model_max_length": 32768,
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| 15 |
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"pad_token": "<|endoftext|>",
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"padding_side": "left",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:a0dd6238841800cad3051f6ed7f8ae3490d5ae1c11477df6d22c08394aa40838
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| 3 |
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size 6161
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