Instructions to use talxcc/Tals-coder-flash-01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use talxcc/Tals-coder-flash-01 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 talxcc/Tals-coder-flash-01
# Run inference directly in the terminal:
llama cli -hf talxcc/Tals-coder-flash-01

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf talxcc/Tals-coder-flash-01
# Run inference directly in the terminal:
llama cli -hf talxcc/Tals-coder-flash-01

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf talxcc/Tals-coder-flash-01
# Run inference directly in the terminal:
./llama-cli -hf talxcc/Tals-coder-flash-01

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf talxcc/Tals-coder-flash-01
# Run inference directly in the terminal:
./build/bin/llama-cli -hf talxcc/Tals-coder-flash-01

Use Docker

docker model run hf.co/talxcc/Tals-coder-flash-01

LM Studio
Jan

vLLM

How to use talxcc/Tals-coder-flash-01 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "talxcc/Tals-coder-flash-01"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "talxcc/Tals-coder-flash-01",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/talxcc/Tals-coder-flash-01

Ollama
How to use talxcc/Tals-coder-flash-01 with Ollama:
```
ollama run hf.co/talxcc/Tals-coder-flash-01
```
Unsloth Desktop

How to use talxcc/Tals-coder-flash-01 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf talxcc/Tals-coder-flash-01

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "talxcc/Tals-coder-flash-01"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use talxcc/Tals-coder-flash-01 with Docker Model Runner:
```
docker model run hf.co/talxcc/Tals-coder-flash-01
```

Lemonade

How to use talxcc/Tals-coder-flash-01 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull talxcc/Tals-coder-flash-01

Run and chat with the model

lemonade run user.Tals-coder-flash-01-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use talxcc/Tals-coder-flash-01 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf talxcc/Tals-coder-flash-01

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 talxcc/Tals-coder-flash-01

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use talxcc/Tals-coder-flash-01 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf talxcc/Tals-coder-flash-01

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 "talxcc/Tals-coder-flash-01" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

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

talxcc commited on 22 days ago

Commit

4259a43

verified ·

1 Parent(s): 22c2c34

Add stock RTX 4060 Ti 16GB non-overclocked benchmark notice

Browse files

Files changed (1) hide show

README.md +7 -0

README.md CHANGED Viewed

@@ -34,6 +34,11 @@ pipeline_tag: text-generation
 ---
 ## 🚀 The Asymmetric Dynamic Quant Breakthrough
 **Tals-coder-flash-01** is a custom **Asymmetric Dynamic Quantization** of **[Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B)**. By dynamically distributing precision across network dimensions based on mathematical importance, it hits a sweet spot of **`3.73 BPW`** (Bits Per Weight) and an ultra-lean **11.86 GB footprint** (compared to 15.2 GB for standard Q4_K_M).
@@ -51,6 +56,8 @@ pipeline_tag: text-generation
 ## 📊 Comprehensive Comparison Table
 | Model Variant | Effective BPW | Model Size | Coding Retention | General Benches (vs Q4_K_M) | Speed (No MTP) | Speed (With MTP) |
 | :--- | :---: | :---: | :---: | :---: | :---: | :---: |
 | **Unsloth Q4_K_M** (Standard) | 4.50 BPW | 15.20 GB | 100% (Baseline) | Baseline (100%) | 19 – 21 tok/s | ~26 – 28 tok/s |

 ---
+> [!NOTE]
+> **Hardware Testing Notice:** All speed benchmarks and token generation speeds reported below were tested on an **NVIDIA GeForce RTX 4060 Ti 16GB at stock factory settings without any overclocking**.
+---
 ## 🚀 The Asymmetric Dynamic Quant Breakthrough
 **Tals-coder-flash-01** is a custom **Asymmetric Dynamic Quantization** of **[Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B)**. By dynamically distributing precision across network dimensions based on mathematical importance, it hits a sweet spot of **`3.73 BPW`** (Bits Per Weight) and an ultra-lean **11.86 GB footprint** (compared to 15.2 GB for standard Q4_K_M).
 ## 📊 Comprehensive Comparison Table
+*(All speed benchmarks measured on a stock, non-overclocked NVIDIA GeForce RTX 4060 Ti 16GB)*
 | Model Variant | Effective BPW | Model Size | Coding Retention | General Benches (vs Q4_K_M) | Speed (No MTP) | Speed (With MTP) |
 | :--- | :---: | :---: | :---: | :---: | :---: | :---: |
 | **Unsloth Q4_K_M** (Standard) | 4.50 BPW | 15.20 GB | 100% (Baseline) | Baseline (100%) | 19 – 21 tok/s | ~26 – 28 tok/s |