Instructions to use programmer-666/Ornith-1.0-35B-MLX-oQ7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use programmer-666/Ornith-1.0-35B-MLX-oQ7 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("programmer-666/Ornith-1.0-35B-MLX-oQ7")

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
LM Studio

How to use programmer-666/Ornith-1.0-35B-MLX-oQ7 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "programmer-666/Ornith-1.0-35B-MLX-oQ7"

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": "programmer-666/Ornith-1.0-35B-MLX-oQ7"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use programmer-666/Ornith-1.0-35B-MLX-oQ7 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "programmer-666/Ornith-1.0-35B-MLX-oQ7"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "programmer-666/Ornith-1.0-35B-MLX-oQ7"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "programmer-666/Ornith-1.0-35B-MLX-oQ7",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use programmer-666/Ornith-1.0-35B-MLX-oQ7 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 "programmer-666/Ornith-1.0-35B-MLX-oQ7"

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 programmer-666/Ornith-1.0-35B-MLX-oQ7

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use programmer-666/Ornith-1.0-35B-MLX-oQ7 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "programmer-666/Ornith-1.0-35B-MLX-oQ7"

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 "programmer-666/Ornith-1.0-35B-MLX-oQ7" \
  --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"

programmer-666 commited on Jul 12

Commit

ad88aaa

verified ·

1 Parent(s): 00e1121

Update README.md

Browse files

Files changed (1) hide show

README.md +119 -120

README.md CHANGED Viewed

@@ -13,127 +13,126 @@ tags:
 - qwen35moe
 - apple-silicon
 - speculative-decoding
-- multi-token-prediction
 model-index:
-  - name: Ornith-1.0-35B-oQ7
-    results:
-      - task:
-          type: text-generation
-        dataset:
-          name: MMLU
-          type: cais/mmlu
-        metrics:
-          - name: Accuracy (30-sample, thinking enabled)
-            type: accuracy
-            value: 86.7
-      - task:
-          type: text-generation
-        dataset:
-          name: MMLU-Pro
-          type: TIGER-Lab/MMLU-Pro
-        metrics:
-          - name: Accuracy (30-sample, thinking enabled)
-            type: accuracy
-            value: 76.7
-      - task:
-          type: text-generation
-        dataset:
-          name: HellaSwag
-          type: Rowan/hellaswag
-        metrics:
-          - name: Accuracy (30-sample, thinking enabled)
-            type: accuracy
-            value: 86.7
-      - task:
-          type: text-generation
-        dataset:
-          name: TruthfulQA
-          type: truthful_qa
-        metrics:
-          - name: Accuracy (30-sample, thinking enabled)
-            type: accuracy
-            value: 90.0
-      - task:
-          type: text-generation
-        dataset:
-          name: ARC Challenge
-          type: allenai/ai2_arc
-        metrics:
-          - name: Accuracy (30-sample, thinking enabled)
-            type: accuracy
-            value: 93.3
-      - task:
-          type: text-generation
-        dataset:
-          name: Winogrande
-          type: winogrande
-        metrics:
-          - name: Accuracy (30-sample, thinking enabled)
-            type: accuracy
-            value: 86.7
-      - task:
-          type: text-generation
-        dataset:
-          name: GSM8K
-          type: openai/gsm8k
-        metrics:
-          - name: Accuracy (30-sample, thinking enabled)
-            type: accuracy
-            value: 96.7
-      - task:
-          type: text-generation
-        dataset:
-          name: MathQA
-          type: math_qa
-        metrics:
-          - name: Accuracy (30-sample, thinking enabled)
-            type: accuracy
-            value: 93.3
-      - task:
-          type: text-generation
-        dataset:
-          name: HumanEval
-          type: openai/openai_humaneval
-        metrics:
-          - name: Pass@1 (30-sample, thinking enabled)
-            type: code_eval
-            value: 90.0
-      - task:
-          type: text-generation
-        dataset:
-          name: MBPP
-          type: google-research-datasets/mbpp
-        metrics:
-          - name: Pass@1 (30-sample, thinking enabled)
-            type: code_eval
-            value: 90.0
-      - task:
-          type: text-generation
-        dataset:
-          name: LiveCodeBench
-          type: livecodebench/code_generation_lite
-        metrics:
-          - name: Accuracy (30-sample, thinking enabled)
-            type: code_eval
-            value: 63.3
-      - task:
-          type: text-generation
-        dataset:
-          name: BBQ
-          type: herojhc/bbq
-        metrics:
-          - name: Accuracy (30-sample, thinking enabled)
-            type: accuracy
-            value: 93.3
-      - task:
-          type: text-generation
-        dataset:
-          name: SafetyBench
-          type: thu-coai/SafetyBench
-        metrics:
-          - name: Accuracy (30-sample, thinking enabled)
-            type: accuracy
-            value: 86.7
 ---
 # Ornith-1.0-35B-oQ7

 - qwen35moe
 - apple-silicon
 - speculative-decoding
 model-index:
+- name: Ornith-1.0-35B-oQ7
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      name: MMLU
+      type: cais/mmlu
+    metrics:
+    - name: Accuracy (30-sample, thinking enabled)
+      type: accuracy
+      value: 86.7
+  - task:
+      type: text-generation
+    dataset:
+      name: MMLU-Pro
+      type: TIGER-Lab/MMLU-Pro
+    metrics:
+    - name: Accuracy (30-sample, thinking enabled)
+      type: accuracy
+      value: 76.7
+  - task:
+      type: text-generation
+    dataset:
+      name: HellaSwag
+      type: Rowan/hellaswag
+    metrics:
+    - name: Accuracy (30-sample, thinking enabled)
+      type: accuracy
+      value: 86.7
+  - task:
+      type: text-generation
+    dataset:
+      name: TruthfulQA
+      type: truthful_qa
+    metrics:
+    - name: Accuracy (30-sample, thinking enabled)
+      type: accuracy
+      value: 90
+  - task:
+      type: text-generation
+    dataset:
+      name: ARC Challenge
+      type: allenai/ai2_arc
+    metrics:
+    - name: Accuracy (30-sample, thinking enabled)
+      type: accuracy
+      value: 93.3
+  - task:
+      type: text-generation
+    dataset:
+      name: Winogrande
+      type: winogrande
+    metrics:
+    - name: Accuracy (30-sample, thinking enabled)
+      type: accuracy
+      value: 86.7
+  - task:
+      type: text-generation
+    dataset:
+      name: GSM8K
+      type: openai/gsm8k
+    metrics:
+    - name: Accuracy (30-sample, thinking enabled)
+      type: accuracy
+      value: 96.7
+  - task:
+      type: text-generation
+    dataset:
+      name: MathQA
+      type: math_qa
+    metrics:
+    - name: Accuracy (30-sample, thinking enabled)
+      type: accuracy
+      value: 93.3
+  - task:
+      type: text-generation
+    dataset:
+      name: HumanEval
+      type: openai/openai_humaneval
+    metrics:
+    - name: Pass@1 (30-sample, thinking enabled)
+      type: code_eval
+      value: 90
+  - task:
+      type: text-generation
+    dataset:
+      name: MBPP
+      type: google-research-datasets/mbpp
+    metrics:
+    - name: Pass@1 (30-sample, thinking enabled)
+      type: code_eval
+      value: 90
+  - task:
+      type: text-generation
+    dataset:
+      name: LiveCodeBench
+      type: livecodebench/code_generation_lite
+    metrics:
+    - name: Accuracy (30-sample, thinking enabled)
+      type: code_eval
+      value: 63.3
+  - task:
+      type: text-generation
+    dataset:
+      name: BBQ
+      type: herojhc/bbq
+    metrics:
+    - name: Accuracy (30-sample, thinking enabled)
+      type: accuracy
+      value: 93.3
+  - task:
+      type: text-generation
+    dataset:
+      name: SafetyBench
+      type: thu-coai/SafetyBench
+    metrics:
+    - name: Accuracy (30-sample, thinking enabled)
+      type: accuracy
+      value: 86.7
 ---
 # Ornith-1.0-35B-oQ7