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Model: steadyflow/Phi-4-reasoning-AWQ Source: Original Platform
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README.md
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README.md
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---
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license: mit
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base_model: microsoft/Phi-4-reasoning
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tags:
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- phi
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- phi4
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- reasoning
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- awq
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- int4
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- quantized
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library_name: transformers
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pipeline_tag: text-generation
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quantization_config:
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quant_method: awq
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bits: 4
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group_size: 128
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zero_point: true
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version: gemm
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---
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# Phi-4-Reasoning AWQ
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AWQ INT4 quantization of [microsoft/Phi-4-reasoning](https://huggingface.co/microsoft/Phi-4-reasoning) (14B parameters).
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## Quantization Details
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| Parameter | Value |
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|-----------|-------|
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| Method | AWQ (Activation-Aware Weight Quantization) |
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| Bit width | 4-bit weights, 16-bit activations (W4A16) |
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| Group size | 128 |
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| Zero point | Enabled |
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| Kernel | GEMM |
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| Calibration | 512 WikiText-2 samples |
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| Source model | `microsoft/Phi-4-reasoning` (~28GB FP16) |
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| Quantized size | ~8.6GB |
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## Usage with vLLM
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```bash
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vllm serve steadyflow/Phi-4-reasoning-AWQ \
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--quantization awq \
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--gpu-memory-utilization 0.90 \
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--max-model-len 4096 \
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--enable-reasoning \
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--reasoning-parser deepseek_r1
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```
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The `--enable-reasoning --reasoning-parser deepseek_r1` flags separate the chain-of-thought (`reasoning` field) from the final answer (`content` field) in the OpenAI-compatible API response.
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> **Known issue:** Some vLLM versions have bugs with Phi-4 reasoning output (infinite loops, missing `<think>` tags). If you encounter this, try setting `VLLM_USE_V1=0` or omit the reasoning flags and parse `<think>...</think>` tags from the output yourself.
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Fits on a single NVIDIA T4 GPU (16GB VRAM).
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## Recommended Inference Parameters
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Per Microsoft's model card, use these parameters for best reasoning quality:
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```python
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temperature = 0.8
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top_p = 0.95
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do_sample = True
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max_new_tokens = 32768 # increase for complex reasoning chains
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```
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## Required System Prompt
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Phi-4 reasoning models require a specific system prompt to activate structured reasoning with `<think>` tags:
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```
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Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: <think> {Thought section} </think> {Solution section}. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion. Now, try to solve the following question through the above guidelines:
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```
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## Usage with Transformers
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```python
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from awq import AutoAWQForCausalLM
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from transformers import AutoTokenizer
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model = AutoAWQForCausalLM.from_quantized("steadyflow/Phi-4-reasoning-AWQ")
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tokenizer = AutoTokenizer.from_pretrained("steadyflow/Phi-4-reasoning-AWQ")
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messages = [
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{"role": "system", "content": "<system prompt above>"},
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{"role": "user", "content": "Your question here"},
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]
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inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
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outputs = model.generate(inputs.to(model.device), max_new_tokens=4096, temperature=0.8, top_p=0.95, do_sample=True)
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print(tokenizer.decode(outputs[0]))
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```
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<|im_start|>system<|im_sep|>You are Phi, a language model trained by Microsoft to help users. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: <think> {Thought section} </think> {Solution section}. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion. Now, try to solve the following question through the above guidelines:<|im_end|>{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|im_start|>user<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'assistant') %}{{'<|im_start|>assistant<|im_sep|>'}}{% generation %}{{message['content'] + '<|im_end|>'}}{% endgeneration %}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant<|im_sep|>' }}{% endif %}
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}
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||||
501289
tokenizer.json
Normal file
501289
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
12
tokenizer_config.json
Normal file
12
tokenizer_config.json
Normal file
@@ -0,0 +1,12 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"backend": "tokenizers",
|
||||
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||||
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||||
"eos_token": "<|im_end|>",
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||||
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||||
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|
||||
"pad_token": "<|dummy_85|>",
|
||||
"padding_side": "left",
|
||||
"tokenizer_class": "TokenizersBackend"
|
||||
}
|
||||
Reference in New Issue
Block a user