Instructions to use micymike/codemate-qwen-1.5B-8k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use micymike/codemate-qwen-1.5B-8k with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="micymike/codemate-qwen-1.5B-8k")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("micymike/codemate-qwen-1.5B-8k")
model = AutoModelForCausalLM.from_pretrained("micymike/codemate-qwen-1.5B-8k", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use micymike/codemate-qwen-1.5B-8k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "micymike/codemate-qwen-1.5B-8k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "micymike/codemate-qwen-1.5B-8k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/micymike/codemate-qwen-1.5B-8k

SGLang

How to use micymike/codemate-qwen-1.5B-8k 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 "micymike/codemate-qwen-1.5B-8k" \
    --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": "micymike/codemate-qwen-1.5B-8k",
		"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 "micymike/codemate-qwen-1.5B-8k" \
        --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": "micymike/codemate-qwen-1.5B-8k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use micymike/codemate-qwen-1.5B-8k with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for micymike/codemate-qwen-1.5B-8k to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for micymike/codemate-qwen-1.5B-8k to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for micymike/codemate-qwen-1.5B-8k to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="micymike/codemate-qwen-1.5B-8k",
    max_seq_length=2048,
)

Docker Model Runner
How to use micymike/codemate-qwen-1.5B-8k with Docker Model Runner:
```
docker model run hf.co/micymike/codemate-qwen-1.5B-8k
```

codemate-qwen-1.5B-8k / README.md

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metadata

license: apache-2.0
base_model: micymike/codemate-qwen-1.5B
tags:
  - text-generation
  - coding
  - conversational
  - python
  - react
  - unsloth
pipeline_tag: text-generation
library_name: transformers
language:
  - en

CodeMate-Qwen-1.5B-8k 🚀

CodeMate-Qwen-1.5B-8k is a multi-turn, conversational coding assistant built on top of micymike/codemate-qwen-1.5B.

This iteration specifically resolves two critical limitations found in the previous model: it expands the core Context Window to 8,192 tokens using dynamic RoPE scaling, and it completely eliminates the prompt-repetition bug using targeted completion-only loss masking during Supervised Fine-Tuning (SFT).

Model Improvements 🛠️

Extended Token Window: Upgraded from 2,048 tokens to 8,192 tokens, allowing it to read massive source code files and retain memory across deep, back-and-forth chat debug sessions.
Prompt Echoing Fixed: Trained natively using Hugging Face TRL's completion_only_loss=True. The model no longer mimics or repeats user instructions before outputting code.
Bug-Fixing Specialization: Fine-tuned on multi-turn code repair paths (iamtarun/code_instructions_120k_alpaca), teaching the model how to isolate programming syntax bugs, explain the root issues, and provide production-ready solutions without forgetting its baseline Python and ReactJS knowledge.

Chat Prompt Format (ChatML) 📝

This model utilizes the standard ChatML prompt template structure. To prevent hallucination or parsing drops, structure your inference payloads like this:

<|im_start|>user
[Your Python or React coding/debugging prompt goes here]<|im_end|>
<|im_start|>assistant

Quickstart with Transformers 🐍

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "micymike/codemate-qwen-1.5B-8k"

model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)

messages = [
    {"role": "user", "content": "Fix the bug in this React code where the state hook updates infinitely."}
]

inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to("cuda")
outputs = model.generate(inputs, max_new_tokens=1024, do_sample=True, temperature=0.3)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Acknowledgements 🎓

Special thanks to the Unsloth AI framework for enabling memory-efficient 8k attention matrix mapping directly inside standard consumer GPU runtimes.