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Model Details
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micymike
Model Tree
micymike/codemate-qwen-1.5B-8k
Input Modalities
Output Modalities
Supported Functionality
micymike/codemate-qwen-1.5B-8kThis model has been configured for a 32K context window using YaRN RoPE scaling.
python
from transformers import AutoConfig config = AutoConfig.from_pretrained( "micymike/CodeMate-Qwen-1.5B-32K-Distilled-on-Claude-Fable-5") print(config.max_position_embeddings)print(config.rope_scaling)Current configuration:
python
{ "rope_type": "yarn", "factor": 4.0, "original_max_position_embeddings": 8192, "rope_theta": 1000000.0}Note: Long-context performance beyond the original context length should be evaluated carefully for specific workloads.
The model was trained on converted Claude Fable 5 coding traces formatted into OpenAI-style conversations.
python
from transformers import AutoTokenizer, AutoModelForCausalLMimport torch model_id = "micymike/CodeMate-Qwen-1.5B-32K-Distilled-on-Claude-Fable-5" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto") messages = [ { "role": "system", "content": "You are CodeMate, an expert programming assistant." }, { "role": "user", "content": "Write a Python function to compute edit distance." }] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True,) inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate( **inputs, max_new_tokens=512, temperature=0.7, top_p=0.9, do_sample=True) print(tokenizer.decode(outputs[0], skip_special_tokens=True))Built upon:
This project is an independent research effort and is not affiliated with or endorsed by Anthropic, Claude, Alibaba, or Qwen.
Built by micymike 🇰🇪
from transformers import AutoConfig config = AutoConfig.from_pretrained( "micymike/CodeMate-Qwen-1.5B-32K-Distilled-on-Claude-Fable-5") print(config.max_position_embeddings)print(config.rope_scaling){ "rope_type": "yarn", "factor": 4.0, "original_max_position_embeddings": 8192, "rope_theta": 1000000.0}from transformers import AutoTokenizer, AutoModelForCausalLMimport torch model_id = "micymike/CodeMate-Qwen-1.5B-32K-Distilled-on-Claude-Fable-5" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto") messages = [ { "role": "system", "content": "You are CodeMate, an expert programming assistant." }, { "role": "user", "content": "Write a Python function to compute edit distance." }] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True,) inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate( **inputs, max_new_tokens=512, temperature=0.7, top_p=0.9, do_sample=True) print(tokenizer.decode(outputs[0], skip_special_tokens=True))