Instructions to use LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1",
	filename="ornith-1.0-35b-IQ4_XS-MTP-graft-headQ6.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 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 LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M

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 LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M

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 LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M

Use Docker

docker model run hf.co/LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M

LM Studio
Jan

vLLM

How to use LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M

Ollama
How to use LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 with Ollama:
```
ollama run hf.co/LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M
```

Unsloth Studio

How to use LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 to start chatting

How to use LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M

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 LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M

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 "LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M" \
  --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"

Docker Model Runner
How to use LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 with Docker Model Runner:
```
docker model run hf.co/LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M
```

Lemonade

How to use LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LordNeel/Ornith-1.0-35B-GGUF-llamacpp-tp1:Q4_K_M

Run and chat with the model

lemonade run user.Ornith-1.0-35B-GGUF-llamacpp-tp1-Q4_K_M

List all available models

lemonade list

Ornith-1.0-35B-GGUF-llamacpp-tp1 / SFT_REMEDIATION_PLAN.md

LordNeel

Add report-only SFT resume preflight guard

3a73c1d verified 26 days ago

preview code

Raw

History Blame

12 kB

SFT Remediation Plan

Generated: 2026-06-27

The production LoRA run was stopped at user request. The latest durable checkpoint, checkpoint-8000, loaded correctly but failed the behavior gate. This plan documents the CPU-side fix path before any future GPU training is started.

Evidence

Gate failures from runs/eval-lora-behavior-checkpoint-8000-fixed.gate.log:
- short_python_add: finished by length instead of stop.
- json_instruction_following: did not parse as JSON.
- json_instruction_following: emitted a markdown fence.
- template_marker_leak: exact one-word prompt produced explanatory text beginning with Here's a thinking process:.
Production data validation passed structurally, with 44,000 train rows and 2,000 eval rows.
Format audit shows the production message data is strongly biased toward explanatory fenced answers:
- 100% of assistant answers contain a code fence.
- About 56% contain Here's.
- About 28% of train answers start with Here's.
- 0% are short exact answers under 80 characters.
- Rendered rows contain empty <think></think> blocks on every assistant answer.

Likely Cause

The checkpoint learned coding-task format, but not strict-output compliance. The data is useful for code synthesis, yet it heavily rewards explanatory responses, markdown fences, and non-short answers. That conflicts with serving requirements where prompts ask for exact code only, compact JSON, or a single word.

Next Training Attempt

Do not resume the stopped run blindly. If training is resumed from checkpoint-8000, first add a repair stage or rebuild the blend.

Recommended blend:

80-90% high-quality coding instruction data.
5-10% strict final-answer/code-only examples without markdown fences.
3-5% JSON-only and schema-following examples.
2-5% exact short-answer examples.
Optional small negative/contrastive set for no markdown fences, no reasoning preambles, and no template markers.

CPU-side repair artifacts are now prepared for this path:

scripts/build_sft_repair_set.py
scripts/build_sft_code_only_from_fences.py
scripts/build_sft_blend.py
scripts/preflight_sft_resume.py
configs/sft_repair_blend.yaml
data/train/coding_sft_repair_messages.jsonl
data/eval/coding_sft_repair_eval_messages.jsonl
data/train/coding_sft_code_only_messages.jsonl
data/eval/coding_sft_code_only_eval_messages.jsonl
data/train/coding_sft_prod_repair_messages.jsonl
data/eval/coding_sft_prod_repair_eval_messages.jsonl
data/train/coding_sft_prod_repair.jsonl
data/eval/coding_sft_prod_repair_eval.jsonl
data/train/coding_sft_prod_repair_strong_messages.jsonl
data/eval/coding_sft_prod_repair_strong_eval_messages.jsonl
data/train/coding_sft_prod_repair_strong.jsonl
data/eval/coding_sft_prod_repair_strong_eval.jsonl
runs/sft-repair-set.json
runs/sft-code-only-extract.json
runs/sft-repair-blend-train.json
runs/sft-repair-blend-eval.json
runs/sft-repair-strong-blend-train.json
runs/sft-repair-strong-blend-eval.json
runs/sft-data-validation-prod-repair.json
runs/sft-format-audit-prod-repair.json
runs/sft-format-gate-strict-repair.json
runs/sft-format-gate-prod-repair.json
runs/sft-data-validation-prod-repair-strong.json
runs/sft-format-audit-prod-repair-strong.json
runs/sft-format-gate-code-only-extract.json
runs/sft-format-gate-prod-repair-strong.json
runs/sft-resume-preflight-strong.json

The generated repair component has 4,096 train rows and 512 eval rows. It has 0 code fences, 0 Here's/Here is preambles, 0 reasoning tags, and about 81% short assistant answers under 80 characters. The blended train split has 44,096 rows and validates with no blocking issues; 107 train rows and 7 eval rows are longer than 1024 tokens, so they are warnings only. The blended train format signals improve from the stopped production mix:

code fences: 100% -> 90.71%
Here's/Here is: about 56% -> 50.93%
short exact answers under 80 chars: 0% -> 7.55%

The reusable format gates pass:

strict repair messages: 8/8 checks, 0 issues.
blended production-plus-repair messages: 10/10 checks, 0 issues.
extracted code-only messages: 8/8 checks, 0 issues.
stronger production-plus-code-only-plus-strict-repair messages: 10/10 checks, 0 issues.

The stronger repair blend is the recommended future SFT input. It keeps the same 44,096 train rows and 2,312 eval rows, but replaces part of the explanatory fenced base data with extracted compilable Python code blocks. Compared with the original production mix, train message format signals improve to:

code fences: 100% -> 54.43%
Here's/Here is: about 56% -> 30.49%
starts with Here's: about 28% -> 15.07%
short exact answers under 80 chars: 0% -> 8.89%

Recommended gates before using GPU:

.venv-lab/bin/python scripts/build_sft_repair_set.py \
  --train-size 4096 \
  --eval-size 512 \
  --seed 29 \
  --train-output data/train/coding_sft_repair_messages.jsonl \
  --eval-output data/eval/coding_sft_repair_eval_messages.jsonl \
  --report-output runs/sft-repair-set.json

.venv-lab/bin/python scripts/build_sft_code_only_from_fences.py \
  --train-size 20000 \
  --eval-size 1000 \
  --seed 41 \
  --train-output data/train/coding_sft_code_only_messages.jsonl \
  --eval-output data/eval/coding_sft_code_only_eval_messages.jsonl \
  --report-output runs/sft-code-only-extract.json

.venv-lab/bin/python scripts/build_sft_blend.py \
  --source data/train/coding_sft_prod_messages.jsonl=40000:base_coding \
  --source data/train/coding_sft_repair_messages.jsonl=4096:strict_repair \
  --output data/train/coding_sft_prod_repair_messages.jsonl \
  --report-output runs/sft-repair-blend-train.json \
  --seed 31

.venv-lab/bin/python scripts/build_sft_blend.py \
  --source data/eval/coding_sft_prod_eval_messages.jsonl=1800:base_coding \
  --source data/eval/coding_sft_repair_eval_messages.jsonl=512:strict_repair \
  --output data/eval/coding_sft_prod_repair_eval_messages.jsonl \
  --report-output runs/sft-repair-blend-eval.json \
  --seed 37

.venv-lab/bin/python scripts/build_sft_blend.py \
  --source data/train/coding_sft_prod_messages.jsonl=24000:base_coding \
  --source data/train/coding_sft_code_only_messages.jsonl=16000:code_only_extract \
  --source data/train/coding_sft_repair_messages.jsonl=4096:strict_repair \
  --output data/train/coding_sft_prod_repair_strong_messages.jsonl \
  --report-output runs/sft-repair-strong-blend-train.json \
  --seed 43

.venv-lab/bin/python scripts/build_sft_blend.py \
  --source data/eval/coding_sft_prod_eval_messages.jsonl=1000:base_coding \
  --source data/eval/coding_sft_code_only_eval_messages.jsonl=800:code_only_extract \
  --source data/eval/coding_sft_repair_eval_messages.jsonl=512:strict_repair \
  --output data/eval/coding_sft_prod_repair_strong_eval_messages.jsonl \
  --report-output runs/sft-repair-strong-blend-eval.json \
  --seed 47

.venv-lab/bin/python scripts/render_sft_jsonl.py \
  --input data/train/coding_sft_prod_repair_messages.jsonl \
  --output data/train/coding_sft_prod_repair.jsonl \
  --force

.venv-lab/bin/python scripts/render_sft_jsonl.py \
  --input data/eval/coding_sft_prod_repair_eval_messages.jsonl \
  --output data/eval/coding_sft_prod_repair_eval.jsonl \
  --force

.venv-lab/bin/python scripts/render_sft_jsonl.py \
  --input data/train/coding_sft_prod_repair_strong_messages.jsonl \
  --output data/train/coding_sft_prod_repair_strong.jsonl \
  --force

.venv-lab/bin/python scripts/render_sft_jsonl.py \
  --input data/eval/coding_sft_prod_repair_strong_eval_messages.jsonl \
  --output data/eval/coding_sft_prod_repair_strong_eval.jsonl \
  --force

.venv-lab/bin/python scripts/validate_sft_jsonl.py \
  --train-jsonl data/train/coding_sft_prod_repair.jsonl \
  --eval-jsonl data/eval/coding_sft_prod_repair_eval.jsonl \
  --max-length 1024 \
  --min-train-rows 44096 \
  --require-license \
  --output runs/sft-data-validation-prod-repair.json

.venv-lab/bin/python scripts/validate_sft_jsonl.py \
  --train-jsonl data/train/coding_sft_prod_repair_strong.jsonl \
  --eval-jsonl data/eval/coding_sft_prod_repair_strong_eval.jsonl \
  --max-length 1024 \
  --min-train-rows 44096 \
  --require-license \
  --output runs/sft-data-validation-prod-repair-strong.json

.venv-lab/bin/python scripts/audit_sft_format.py \
  --input data/train/coding_sft_repair_messages.jsonl \
  --input data/eval/coding_sft_repair_eval_messages.jsonl \
  --input data/train/coding_sft_prod_repair_messages.jsonl \
  --input data/eval/coding_sft_prod_repair_eval_messages.jsonl \
  --input data/train/coding_sft_prod_repair.jsonl \
  --input data/eval/coding_sft_prod_repair_eval.jsonl \
  --output runs/sft-format-audit-prod-repair.json

.venv-lab/bin/python scripts/audit_sft_format.py \
  --input data/train/coding_sft_code_only_messages.jsonl \
  --input data/eval/coding_sft_code_only_eval_messages.jsonl \
  --input data/train/coding_sft_prod_repair_strong_messages.jsonl \
  --input data/eval/coding_sft_prod_repair_strong_eval_messages.jsonl \
  --input data/train/coding_sft_prod_repair_strong.jsonl \
  --input data/eval/coding_sft_prod_repair_strong_eval.jsonl \
  --output runs/sft-format-audit-prod-repair-strong.json

.venv-lab/bin/python scripts/check_sft_format_gates.py \
  --audit-report runs/sft-format-audit-prod-repair.json \
  --config configs/sft_format_gates.yaml \
  --profile strict_repair_messages \
  --output runs/sft-format-gate-strict-repair.json

.venv-lab/bin/python scripts/check_sft_format_gates.py \
  --audit-report runs/sft-format-audit-prod-repair.json \
  --config configs/sft_format_gates.yaml \
  --profile prod_repair_messages \
  --output runs/sft-format-gate-prod-repair.json

.venv-lab/bin/python scripts/check_sft_format_gates.py \
  --audit-report runs/sft-format-audit-prod-repair-strong.json \
  --config configs/sft_format_gates.yaml \
  --profile code_only_extract_messages \
  --output runs/sft-format-gate-code-only-extract.json

.venv-lab/bin/python scripts/check_sft_format_gates.py \
  --audit-report runs/sft-format-audit-prod-repair-strong.json \
  --config configs/sft_format_gates.yaml \
  --profile prod_repair_strong_messages \
  --output runs/sft-format-gate-prod-repair-strong.json

.venv-lab/bin/python scripts/preflight_sft_resume.py \
  --output runs/sft-resume-preflight-strong.json

The preflight guard is report-only and does not launch training. Current result: 16/16 checks pass, 0 warnings, 0 failures. It verifies the stop record, checkpoint-8000 files, strong repair row counts, validation report, format gates, no matching training/serving/eval/quantization processes, and idle GPUs.

Required release gate for every future checkpoint:

CUDA_VISIBLE_DEVICES=0 \
.venv-train/bin/python scripts/eval_lora_behavior.py \
  --base-model deepreinforce-ai/Ornith-1.0-35B \
  --adapter artifacts/train/ornith-35b-coding-lora-prod-20k/checkpoint-<step> \
  --suite configs/model_behavior_suite.yaml \
  --output runs/eval-lora-behavior-checkpoint-<step>.json

scripts/eval_lora_behavior.py now defaults to final-answer mode by passing enable_thinking=False into the chat template. Use --enable-thinking only for explicit reasoning-mode experiments. This matches the published llama.cpp REASONING=off serving profile.

Release Rule

No LoRA adapter should be uploaded unless high/critical behavior checks pass. For this project, that means at minimum:

simple code-only prompt finishes by stop;
compact JSON prompt parses as JSON and has no markdown fence;
exact one-word prompt returns exactly the requested word;
no Qwen markers or <think> tags leak into final content.

Current Status

No fine-tuned adapter is released. The only durable SFT checkpoint is checkpoint-8000, and it is not a release candidate.

SFT remains stopped until explicitly resumed. The prepared repair blend is CPU work only and does not consume GPU memory.