Text Generation
Transformers
Safetensors
qwen2
reasoning
grpo
reinforcement-learning
rlvr
r1-zero
countdown
math
conversational
Eval Results (legacy)
text-generation-inference
Instructions to use Bluebox85033/cogito-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bluebox85033/cogito-3b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Bluebox85033/cogito-3b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Bluebox85033/cogito-3b") model = AutoModelForCausalLM.from_pretrained("Bluebox85033/cogito-3b", 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 Bluebox85033/cogito-3b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Bluebox85033/cogito-3b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Bluebox85033/cogito-3b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Bluebox85033/cogito-3b
- SGLang
How to use Bluebox85033/cogito-3b 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 "Bluebox85033/cogito-3b" \ --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": "Bluebox85033/cogito-3b", "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 "Bluebox85033/cogito-3b" \ --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": "Bluebox85033/cogito-3b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Bluebox85033/cogito-3b with Docker Model Runner:
docker model run hf.co/Bluebox85033/cogito-3b
Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- README.md +29 -0
- chat_template.jinja +54 -0
- config.json +70 -0
- generation_config.json +9 -0
- model.safetensors +3 -0
- tokenizer.json +3 -0
- tokenizer_config.json +32 -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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*.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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license: apache-2.0
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base_model: Qwen/Qwen2.5-3B
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library_name: transformers
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tags:
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- grpo
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- reasoning
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- rl
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- reinforcement-learning
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- cogito
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---
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# Cogito - a reasoning model trained with GRPO (RL from verifiable rewards)
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Cogito is `Qwen/Qwen2.5-3B` after GRPO reasoning training on Countdown + math, with no reasoning supervision - only correctness rewards. It learns to produce a `<think>...</think>` chain-of-thought before answering, and response length grows over training (the DeepSeek-R1 signature).
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## Results
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| Benchmark (held-out) | Base | Cogito |
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|---|---|---|
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| Countdown solve rate (pass@1) | 7.8% | 64.1% |
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| GSM8K pass@1 | 81.0% | 82.3% |
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| MATH-500 pass@1 | 55.0% | 64.3% |
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## Method
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GRPO (group-relative, no critic). Per step: sample G rollouts per prompt with vLLM, score with verifiable rewards (Countdown: expression evaluates to target using each number once; math: boxed answer matches), compute group-relative advantages, update.
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Recipe and eval harness: see the `recipe/` folder in this repo.
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are a helpful assistant.' }}
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{%- endif %}
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{{- "\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>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\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" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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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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| 5 |
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"attention_dropout": 0.0,
|
| 6 |
+
"bos_token_id": null,
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| 7 |
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"dtype": "float32",
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| 8 |
+
"eos_token_id": 151643,
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| 9 |
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"hidden_act": "silu",
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| 10 |
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"hidden_size": 2048,
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| 11 |
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"initializer_range": 0.02,
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| 12 |
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"intermediate_size": 11008,
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| 13 |
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"layer_types": [
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"full_attention",
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"full_attention",
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| 16 |
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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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"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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| 52 |
+
"max_window_layers": 36,
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| 53 |
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"model_type": "qwen2",
|
| 54 |
+
"num_attention_heads": 16,
|
| 55 |
+
"num_hidden_layers": 36,
|
| 56 |
+
"num_key_value_heads": 2,
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| 57 |
+
"pad_token_id": 151643,
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| 58 |
+
"rms_norm_eps": 1e-06,
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| 59 |
+
"rope_parameters": {
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| 60 |
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"rope_theta": 1000000.0,
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"rope_type": "default"
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},
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| 63 |
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"sliding_window": null,
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| 64 |
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"tie_word_embeddings": true,
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| 65 |
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"transformers_version": "5.12.1",
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| 66 |
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"use_cache": false,
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| 67 |
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"use_mrope": false,
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| 68 |
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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
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{
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"do_sample": false,
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| 3 |
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"eos_token_id": [
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151643
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| 5 |
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],
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| 6 |
+
"max_new_tokens": 2048,
|
| 7 |
+
"pad_token_id": 151643,
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| 8 |
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"transformers_version": "5.12.1"
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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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| 2 |
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oid sha256:d6d101f308a64a609ef4d9d27cbfd97e66a38664e23a335f17a570eaed1c9240
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size 12343804296
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
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size 11421892
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tokenizer_config.json
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{
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| 2 |
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"add_prefix_space": false,
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"backend": "tokenizers",
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| 4 |
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"bos_token": null,
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| 5 |
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"clean_up_tokenization_spaces": false,
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| 6 |
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"eos_token": "<|endoftext|>",
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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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| 10 |
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"<|im_end|>",
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| 11 |
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"<|object_ref_start|>",
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| 12 |
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"<|object_ref_end|>",
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| 13 |
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"<|box_start|>",
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| 14 |
+
"<|box_end|>",
|
| 15 |
+
"<|quad_start|>",
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| 16 |
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"<|quad_end|>",
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| 17 |
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"<|vision_start|>",
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| 18 |
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"<|vision_end|>",
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| 19 |
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"<|vision_pad|>",
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| 20 |
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"<|image_pad|>",
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| 21 |
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"<|video_pad|>"
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| 22 |
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],
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| 23 |
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"is_local": true,
|
| 24 |
+
"local_files_only": false,
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| 25 |
+
"model_max_length": 131072,
|
| 26 |
+
"pad_token": "<|endoftext|>",
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| 27 |
+
"padding_side": "left",
|
| 28 |
+
"split_special_tokens": false,
|
| 29 |
+
"tokenizer_class": "Qwen2Tokenizer",
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| 30 |
+
"truncation_side": "left",
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| 31 |
+
"unk_token": null
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| 32 |
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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 |
+
oid sha256:98797455fe78d58d558b93b3219d683f5595521dcacdf52aa2e85f4cb188fd93
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| 3 |
+
size 7569
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