Instructions to use continuum-ai/qwen2.5-coder-7b-compacted with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use continuum-ai/qwen2.5-coder-7b-compacted with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("continuum-ai/qwen2.5-coder-7b-compacted")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use continuum-ai/qwen2.5-coder-7b-compacted with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "continuum-ai/qwen2.5-coder-7b-compacted"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "continuum-ai/qwen2.5-coder-7b-compacted"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use continuum-ai/qwen2.5-coder-7b-compacted with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "continuum-ai/qwen2.5-coder-7b-compacted"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "continuum-ai/qwen2.5-coder-7b-compacted"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "continuum-ai/qwen2.5-coder-7b-compacted",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use continuum-ai/qwen2.5-coder-7b-compacted with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "continuum-ai/qwen2.5-coder-7b-compacted"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default continuum-ai/qwen2.5-coder-7b-compacted

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use continuum-ai/qwen2.5-coder-7b-compacted with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "continuum-ai/qwen2.5-coder-7b-compacted"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "continuum-ai/qwen2.5-coder-7b-compacted" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

qwen2.5-coder-7b-compacted / eval /calibrated_eval_results.json

EnricoFermi

Upload eval/calibrated_eval_results.json with huggingface_hub

c8371e7 verified 7 months ago

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{
  "status": "complete",
  "benchmark": "humaneval",
  "anchor": {
    "model": "Qwen/Qwen2.5-Coder-7B",
    "published": {
      "score": 61.6,
      "metric": "pass@1",
      "source": "Qwen2.5-Coder Technical Report Table 5, arXiv:2409.12186"
    },
    "measured_scores": {
      "humaneval": 62.2,
      "humaneval_plus": 53.7
    },
    "delta": 0.6
  },
  "model_under_test": {
    "path": "/home/joel/forge_v2_qwen7b_compensated_kl",
    "measured_scores": {
      "humaneval": 61.0,
      "humaneval_plus": 53.0
    }
  },
  "tolerance": 3.0,
  "calibration_passed": true
}

	{
	"status": "complete",
	"benchmark": "humaneval",
	"anchor": {
	"model": "Qwen/Qwen2.5-Coder-7B",
	"published": {
	"score": 61.6,
	"metric": "pass@1",
	"source": "Qwen2.5-Coder Technical Report Table 5, arXiv:2409.12186"
	},
	"measured_scores": {
	"humaneval": 62.2,
	"humaneval_plus": 53.7
	},
	"delta": 0.6
	},
	"model_under_test": {
	"path": "/home/joel/forge_v2_qwen7b_compensated_kl",
	"measured_scores": {
	"humaneval": 61.0,
	"humaneval_plus": 53.0
	}
	},
	"tolerance": 3.0,
	"calibration_passed": true
	}