Instructions to use cmndcntrlcyber/qwen14b-code-trainer-v9_mixed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cmndcntrlcyber/qwen14b-code-trainer-v9_mixed with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-14B-Instruct")
model = PeftModel.from_pretrained(base_model, "cmndcntrlcyber/qwen14b-code-trainer-v9_mixed")

Transformers

How to use cmndcntrlcyber/qwen14b-code-trainer-v9_mixed with Transformers:

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

pipe = pipeline("text-generation", model="cmndcntrlcyber/qwen14b-code-trainer-v9_mixed")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("cmndcntrlcyber/qwen14b-code-trainer-v9_mixed", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cmndcntrlcyber/qwen14b-code-trainer-v9_mixed with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cmndcntrlcyber/qwen14b-code-trainer-v9_mixed"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cmndcntrlcyber/qwen14b-code-trainer-v9_mixed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cmndcntrlcyber/qwen14b-code-trainer-v9_mixed

SGLang

How to use cmndcntrlcyber/qwen14b-code-trainer-v9_mixed 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 "cmndcntrlcyber/qwen14b-code-trainer-v9_mixed" \
    --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": "cmndcntrlcyber/qwen14b-code-trainer-v9_mixed",
		"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 "cmndcntrlcyber/qwen14b-code-trainer-v9_mixed" \
        --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": "cmndcntrlcyber/qwen14b-code-trainer-v9_mixed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cmndcntrlcyber/qwen14b-code-trainer-v9_mixed with Docker Model Runner:
```
docker model run hf.co/cmndcntrlcyber/qwen14b-code-trainer-v9_mixed
```

qwen14b-code-trainer-v9_mixed / phase4-agent-eval.json

cmndcntrlcyber

V7 agent eval (nexus-aligned): 5/5 making progress

d438ec1 verified 13 days ago

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	{
	"model": "Qwen/Qwen2.5-Coder-14B-Instruct",
	"adapter": "cmndcntrlcyber/qwen14b-code-trainer-v9_mixed",
	"eval_type": "nexus-aligned",
	"scenarios": 5,
	"progressing": 5,
	"progress_rate": 1.0,
	"target_threshold": 3,
	"meets_target": true,
	"tools_tested": [
	"Read",
	"Write",
	"Edit",
	"LS",
	"Bash",
	"Grep",
	"Glob"
	],
	"results": [
	{
	"scenario": "read_and_analyze",
	"description": "Read a file and explain what it does",
	"making_progress": true,
	"used_tools": true,
	"gave_explanation": true,
	"not_refusing": true,
	"not_looping": true,
	"reason": "used tools; gave explanation",
	"n_turns": 5
	},
	{
	"scenario": "find_and_fix_bug",
	"description": "Find a bug in code and propose a fix",
	"making_progress": true,
	"used_tools": true,
	"gave_explanation": true,
	"not_refusing": true,
	"not_looping": true,
	"reason": "used tools; gave explanation",
	"n_turns": 5
	},
	{
	"scenario": "multi_file_investigation",
	"description": "Investigate across multiple files",
	"making_progress": true,
	"used_tools": true,
	"gave_explanation": true,
	"not_refusing": true,
	"not_looping": true,
	"reason": "used tools; gave explanation",
	"n_turns": 5
	},
	{
	"scenario": "write_new_code",
	"description": "Write a new function based on requirements",
	"making_progress": true,
	"used_tools": true,
	"gave_explanation": true,
	"not_refusing": true,
	"not_looping": true,
	"reason": "used tools; gave explanation",
	"n_turns": 3
	},
	{
	"scenario": "run_and_debug",
	"description": "Run a command and interpret the output",
	"making_progress": true,
	"used_tools": true,
	"gave_explanation": true,
	"not_refusing": true,
	"not_looping": true,
	"reason": "used tools; gave explanation",
	"n_turns": 5
	}
	]
	}