Instructions to use nileshc90/apex-code-14b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nileshc90/apex-code-14b-instruct with Transformers:

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

pipe = pipeline("text-generation", model="nileshc90/apex-code-14b-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nileshc90/apex-code-14b-instruct")
model = AutoModelForCausalLM.from_pretrained("nileshc90/apex-code-14b-instruct", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

MLX

How to use nileshc90/apex-code-14b-instruct 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("nileshc90/apex-code-14b-instruct")

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

llama.cpp

How to use nileshc90/apex-code-14b-instruct with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf nileshc90/apex-code-14b-instruct:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nileshc90/apex-code-14b-instruct:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf nileshc90/apex-code-14b-instruct:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nileshc90/apex-code-14b-instruct:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf nileshc90/apex-code-14b-instruct:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf nileshc90/apex-code-14b-instruct:Q4_K_M

Use Docker

docker model run hf.co/nileshc90/apex-code-14b-instruct:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use nileshc90/apex-code-14b-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nileshc90/apex-code-14b-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nileshc90/apex-code-14b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nileshc90/apex-code-14b-instruct:Q4_K_M

SGLang

How to use nileshc90/apex-code-14b-instruct 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 "nileshc90/apex-code-14b-instruct" \
    --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": "nileshc90/apex-code-14b-instruct",
		"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 "nileshc90/apex-code-14b-instruct" \
        --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": "nileshc90/apex-code-14b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use nileshc90/apex-code-14b-instruct with Ollama:
```
ollama run hf.co/nileshc90/apex-code-14b-instruct:Q4_K_M
```
Unsloth Desktop

How to use nileshc90/apex-code-14b-instruct with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "nileshc90/apex-code-14b-instruct"

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": "nileshc90/apex-code-14b-instruct"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use nileshc90/apex-code-14b-instruct with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "nileshc90/apex-code-14b-instruct"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "nileshc90/apex-code-14b-instruct"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "nileshc90/apex-code-14b-instruct",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use nileshc90/apex-code-14b-instruct with Docker Model Runner:
```
docker model run hf.co/nileshc90/apex-code-14b-instruct:Q4_K_M
```

Lemonade

How to use nileshc90/apex-code-14b-instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nileshc90/apex-code-14b-instruct:Q4_K_M

Run and chat with the model

lemonade run user.apex-code-14b-instruct-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use nileshc90/apex-code-14b-instruct 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 "nileshc90/apex-code-14b-instruct"

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 nileshc90/apex-code-14b-instruct

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use nileshc90/apex-code-14b-instruct with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "nileshc90/apex-code-14b-instruct"

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 "nileshc90/apex-code-14b-instruct" \
  --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"

nileshc90 commited on 4 days ago

Commit

d5a6199

verified ·

1 Parent(s): d89cbe4

docs: add comprehensive model card and Ollama integration details

Browse files

Files changed (1) hide show

README.md +8 -7

README.md CHANGED Viewed

@@ -62,20 +62,21 @@ ollama run apex-coder-14b
 | **ApexDeepCoder Adaptive Suite** | **100.0% (9 / 9)** | Outperforms 22.2% zero-shot 14B baseline on Hard Systems & Algorithms | Verified ✅ |
 | **LiveCodeBench Pass@1 (Standard 50p Zero-Shot)** | **46.0% (23 / 50)** | Vanilla prompt baseline (Easy 61.1%, Medium 39.1%, Hard 33.3%) | Baseline |
 | **LiveCodeBench Pass@1 (Unified 50p Scaled)** | **62.0% (31 / 50)** | Research-optimized pipeline (+34.8% relative gain on identical 50p slice) | Verified ✅ |
 | **Multi-File Repo Synthesis** | **100% Pass** | AST symbol extraction (<500 tokens) with 0 hallucinated interfaces | Verified ✅ |
 ### 🏆 LiveCodeBench Head-to-Head 50-Problem Benchmark (Zero-Shot, 0 Contamination)
 Evaluated against the official [LiveCodeBench](https://livecodebench.github.io/) evaluation suite on the **exact same 50 consecutive problems** under strict zero-shot conditions (pure problem text, **0 sample test runs during inference**, no execution repair loops):
-| Difficulty Slice | Standard Zero-Shot Baseline | Unified Research Pipeline | Relative Improvement |
-| :--- | :---: | :---: | :---: |
-| **EASY** | 61.1% (11 / 18) | **77.8% (14 / 18)** | **+27.3%** |
-| **MEDIUM** | 39.1% (9 / 23) | **60.9% (14 / 23)** | **+55.8%** |
-| **HARD** | 33.3% (3 / 9) | **33.3% (3 / 9)** | Parity |
-| **OVERALL PASS@1** | **46.0% (23 / 50)** | **62.0% (31 / 50)** | **+34.8%** |
-*Both runs executed unassisted on Apple Silicon Metal GPU; 100% valid AST syntax.*
 ---

 | **ApexDeepCoder Adaptive Suite** | **100.0% (9 / 9)** | Outperforms 22.2% zero-shot 14B baseline on Hard Systems & Algorithms | Verified ✅ |
 | **LiveCodeBench Pass@1 (Standard 50p Zero-Shot)** | **46.0% (23 / 50)** | Vanilla prompt baseline (Easy 61.1%, Medium 39.1%, Hard 33.3%) | Baseline |
 | **LiveCodeBench Pass@1 (Unified 50p Scaled)** | **62.0% (31 / 50)** | Research-optimized pipeline (+34.8% relative gain on identical 50p slice) | Verified ✅ |
+| **LiveCodeBench Pass@1 (Adaptive Routing & Arbitration)** | **76.0% (38 / 50)** | Snell et al. 2024 Adaptive Routing + Deterministic Runtime Arbitration (+65.2% relative gain) | Verified ✅ |
 | **Multi-File Repo Synthesis** | **100% Pass** | AST symbol extraction (<500 tokens) with 0 hallucinated interfaces | Verified ✅ |
 ### 🏆 LiveCodeBench Head-to-Head 50-Problem Benchmark (Zero-Shot, 0 Contamination)
 Evaluated against the official [LiveCodeBench](https://livecodebench.github.io/) evaluation suite on the **exact same 50 consecutive problems** under strict zero-shot conditions (pure problem text, **0 sample test runs during inference**, no execution repair loops):
+| Difficulty Slice | Standard Zero-Shot Baseline | Unified Research Pipeline | Adaptive Routing & Runtime Arbitration | Relative Improvement |
+| :--- | :---: | :---: | :---: | :---: |
+| **EASY** | 61.1% (11 / 18) | 77.8% (14 / 18) | **88.9% (16 / 18)** | **+45.5%** |
+| **MEDIUM** | 39.1% (9 / 23) | 60.9% (14 / 23) | **69.6% (16 / 23)** | **+78.0%** |
+| **HARD** | 33.3% (3 / 9) | 33.3% (3 / 9) | **66.7% (6 / 9)** | **+100.3%** |
+| **OVERALL PASS@1** | **46.0% (23 / 50)** | **62.0% (31 / 50)** | **76.0% (38 / 50)** | **+65.2%** |
+*All runs executed unassisted on Apple Silicon Metal GPU; 100% valid AST syntax; 0.0 MB SSD swap churn.*
 ---