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)

Inference
Notebooks
Google Colab
Kaggle
Local Apps
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 @mariozechner/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

Hermes Agent new

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

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"}
     ]
   }'

EnricoFermi commited on Apr 8

Commit

d5ac309

verified ·

1 Parent(s): c117684

Upload v2-7b-coder-compensated.alloy.json with huggingface_hub

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v2-7b-coder-compensated.alloy.json +3 -3

v2-7b-coder-compensated.alloy.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
   "name": "v2-7b-coder-compensated",
-  "version": "1.2.0",
   "description": "Methodology validation artifact for the v2 forge pipeline + KL-distillation compensation LoRA. Demonstrates that aggressive head pruning + activation-metric importance + pad-mode defrag, when paired with output-distribution distillation against the unmodified teacher, recovers near-base HumanEval capability (61.0 vs 62.2 base, within calibration tolerance). This is the empirical anchor for PLASTICITY-COMPACTION \u00a74.1.3.3 and the loss-function ablation that closes the \u00a74.1.3.2 PPL/HumanEval disconnect. NOT a Pareto improvement over the unmodified base 7B at any single VRAM tier \u2014 published as proof that the methodology stack works end-to-end, in preparation for the Qwen3.5-35B-A3B and 397B-A17B forges where the pruning dimension actually wins.",
   "author": "continuum-ai",
   "tags": [
@@ -191,9 +191,9 @@
       {
         "target": "huggingface",
         "url": "https://huggingface.co/continuum-ai/v2-7b-coder-compensated",
-        "publishedAt": "2026-04-08T05:01:40.446154+00:00"
       }
     ],
-    "issuedAt": "2026-04-08T05:01:40.446154+00:00"
   }
 }

 {
   "name": "v2-7b-coder-compensated",
+  "version": "1.2.1",
   "description": "Methodology validation artifact for the v2 forge pipeline + KL-distillation compensation LoRA. Demonstrates that aggressive head pruning + activation-metric importance + pad-mode defrag, when paired with output-distribution distillation against the unmodified teacher, recovers near-base HumanEval capability (61.0 vs 62.2 base, within calibration tolerance). This is the empirical anchor for PLASTICITY-COMPACTION \u00a74.1.3.3 and the loss-function ablation that closes the \u00a74.1.3.2 PPL/HumanEval disconnect. NOT a Pareto improvement over the unmodified base 7B at any single VRAM tier \u2014 published as proof that the methodology stack works end-to-end, in preparation for the Qwen3.5-35B-A3B and 397B-A17B forges where the pruning dimension actually wins.",
   "author": "continuum-ai",
   "tags": [
       {
         "target": "huggingface",
         "url": "https://huggingface.co/continuum-ai/v2-7b-coder-compensated",
+        "publishedAt": "2026-04-08T05:02:57.072577+00:00"
       }
     ],
+    "issuedAt": "2026-04-08T05:02:57.072577+00:00"
   }
 }