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

Llama new
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 28 days ago

Commit

cb39b1a

verified ·

1 Parent(s): c329eaa

Update model card to Tals-coder-flash-01

Browse files

Files changed (1) hide show

README.md +9 -9

README.md CHANGED Viewed

@@ -20,7 +20,7 @@ pipeline_tag: text-generation
 <div align="center">
-# ⚡ Tals-code-flash-02 (27B Asymmetric Dynamic Quant + Native MTP)
 ### *99.9% Coding Performance of Q4_K_M at 3.73 BPW with High-Speed MTP Speculative Decoding*
@@ -28,7 +28,7 @@ pipeline_tag: text-generation
   <img src="flash.gif" width="600" alt="The Flash Lightning Speed" />
 </p>
-### *"Why run bloated 15GB Q4 models that crawl at 19 tok/s? Tals-code-flash-02 delivers 99.9% coding fidelity at 34–40 tok/s on consumer GPUs."*
 </div>
@@ -36,7 +36,7 @@ pipeline_tag: text-generation
 ## 🚀 The Asymmetric Dynamic Quant Breakthrough
-**Tals-code-flash-02** 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).
 ### 🎯 Key Highlights:
 * 🏆 **99.9% Coding Retention:** Matches unsloth Q4_K_M within **0.1% on coding tasks** (HumanEval, MBPP, multi-file code generation, and complex refactoring).
@@ -55,13 +55,13 @@ pipeline_tag: text-generation
 | :--- | :---: | :---: | :---: | :---: | :---: | :---: |
 | **Unsloth Q4_K_M** (Standard) | 4.50 BPW | 15.20 GB | 100% (Baseline) | Baseline (100%) | 19 – 21 tok/s | ~26 – 28 tok/s |
 | **Standard Q3_K_L** | 3.52 BPW | 11.40 GB | ~94.2% | -1.8 to -2.4 pts | 21 – 23 tok/s | 29 – 30 tok/s |
-| ⚡ **Tals-code-flash-02** | **`3.73 BPW`** | **11.86 GB** | **99.9%** | **-0.3 to -0.5 pts** | **24 – 26 tok/s** | **34 – 40 tok/s** |
 ---
 ## 💻 Hardware Compatibility & VRAM Sizing Guide
-Because of its lean 11.86 GB footprint, Tals-code-flash-02 unlocks extreme context windows across consumer GPUs without ever touching slow system RAM:
 ### 1. 12 GB VRAM GPUs *(RTX 3060 12GB, RTX 4070 12GB)*
 * **Model Footprint:** 11.86 GB fits completely in VRAM.
@@ -92,7 +92,7 @@ set CUDA_DEVICE_SCHEDULE=BLOCKING_SYNC
 ### 1. 16GB GPUs — Default 128k High-Speed Coding Mode (34–40 tok/s)
 ```bash
 ./llama-server \
-  -m "tals-coder-01.gguf" \
   -ngl 99 \
   -c 128000 \
   -ctk q8_0 -ctv q4_0 \
@@ -106,7 +106,7 @@ set CUDA_DEVICE_SCHEDULE=BLOCKING_SYNC
 ### 2. 16GB GPUs — 64k Full FP16 KV Precision Mode
 ```bash
 ./llama-server \
-  -m "tals-coder-01.gguf" \
   -ngl 99 \
   -c 65536 \
   -ctk f16 -ctv f16 \
@@ -118,7 +118,7 @@ set CUDA_DEVICE_SCHEDULE=BLOCKING_SYNC
 ### 3. 12GB GPUs — 8k Standard VRAM Mode
 ```bash
 ./llama-server \
-  -m "tals-coder-01.gguf" \
   -ngl 99 \
   -c 8192 \
   -ctk q8_0 -ctv q8_0 \
@@ -133,7 +133,7 @@ set CUDA_DEVICE_SCHEDULE=BLOCKING_SYNC
 Connect your favorite coding agent with deterministic settings:
 * **Base URL:** `http://127.0.0.1:8080/v1`
-* **Model Name:** `tals-code-flash-02`
 * **API Key:** `not-needed` (any string)
 * **Temperature:** `0.0` – `0.2` (Deterministic coding & reasoning)

 <div align="center">
+# ⚡ Tals-coder-flash-01 (27B Asymmetric Dynamic Quant + Native MTP)
 ### *99.9% Coding Performance of Q4_K_M at 3.73 BPW with High-Speed MTP Speculative Decoding*
   <img src="flash.gif" width="600" alt="The Flash Lightning Speed" />
 </p>
+### *"Why run bloated 15GB Q4 models that crawl at 19 tok/s? Tals-coder-flash-01 delivers 99.9% coding fidelity at 34–40 tok/s on consumer GPUs."*
 </div>
 ## 🚀 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).
 ### 🎯 Key Highlights:
 * 🏆 **99.9% Coding Retention:** Matches unsloth Q4_K_M within **0.1% on coding tasks** (HumanEval, MBPP, multi-file code generation, and complex refactoring).
 | :--- | :---: | :---: | :---: | :---: | :---: | :---: |
 | **Unsloth Q4_K_M** (Standard) | 4.50 BPW | 15.20 GB | 100% (Baseline) | Baseline (100%) | 19 – 21 tok/s | ~26 – 28 tok/s |
 | **Standard Q3_K_L** | 3.52 BPW | 11.40 GB | ~94.2% | -1.8 to -2.4 pts | 21 – 23 tok/s | 29 – 30 tok/s |
+| ⚡ **Tals-coder-flash-01** | **`3.73 BPW`** | **11.86 GB** | **99.9%** | **-0.3 to -0.5 pts** | **24 – 26 tok/s** | **34 – 40 tok/s** |
 ---
 ## 💻 Hardware Compatibility & VRAM Sizing Guide
+Because of its lean 11.86 GB footprint, Tals-coder-flash-01 unlocks extreme context windows across consumer GPUs without ever touching slow system RAM:
 ### 1. 12 GB VRAM GPUs *(RTX 3060 12GB, RTX 4070 12GB)*
 * **Model Footprint:** 11.86 GB fits completely in VRAM.
 ### 1. 16GB GPUs — Default 128k High-Speed Coding Mode (34–40 tok/s)
 ```bash
 ./llama-server \
+  -m "Tals-coder-flash-01.gguf" \
   -ngl 99 \
   -c 128000 \
   -ctk q8_0 -ctv q4_0 \
 ### 2. 16GB GPUs — 64k Full FP16 KV Precision Mode
 ```bash
 ./llama-server \
+  -m "Tals-coder-flash-01.gguf" \
   -ngl 99 \
   -c 65536 \
   -ctk f16 -ctv f16 \
 ### 3. 12GB GPUs — 8k Standard VRAM Mode
 ```bash
 ./llama-server \
+  -m "Tals-coder-flash-01.gguf" \
   -ngl 99 \
   -c 8192 \
   -ctk q8_0 -ctv q8_0 \
 Connect your favorite coding agent with deterministic settings:
 * **Base URL:** `http://127.0.0.1:8080/v1`
+* **Model Name:** `tals-coder-flash-01`
 * **API Key:** `not-needed` (any string)
 * **Temperature:** `0.0` – `0.2` (Deterministic coding & reasoning)