Pilcothink

Ornith-1.5-9B-MixedInt4-AutoRound

Base Model

Base model: ornith-ai/Ornith-1.5-9B

This model is a quantized version of the original Ornith-1.5-9B checkpoint. It is not a fine-tune, merge, or distillation.

Ornith-1.5-9B is a dense multimodal reasoning model based on the Qwen3.5 architecture.

Quantization

Quantization was performed using Intel AutoRound with a custom mixed-precision quantization configuration.

The quantization recipe was designed to balance:

Model quality
Memory efficiency
Inference performance
vLLM compatibility

Some model components are intentionally retained at higher precision where appropriate.

The calibration process was primarily optimized for instruction-following performance[no-think], focusing on maintaining high-quality responses for user instructions and conversational interactions after quantization.

Table with columns: Property, Value
Property	Value
Quantization framework	Intel AutoRound
Quantization type	Custom Mixed-Precision INT4
Base model	ornith-ai/Ornith-1.5-9B
Architecture	Qwen3.5
Language layers	32
Vision encoder depth	27
Native context length	262,144 tokens
Vision tower	Preserved at original precision

The detailed mixed-precision allocation strategy is not included in this model card.

Evaluation

Evaluation was performed using AutoRound's evaluation interface with LM Evaluation Harness.

The following results compare the original Ornith-1.5-9B model against Ornith-1.5-9B-MixedInt4-AutoRound.

Table with columns: Benchmark, Metric, Ornith-1.5-9B, Ornith-1.5-9B-MixedInt4-AutoRound, Difference, Recovery Rate
Benchmark	Metric	Ornith-1.5-9B	Ornith-1.5-9B-MixedInt4-AutoRound	Difference	Recovery Rate
MMLU	acc	77.30%	76.80%	-0.50 pp	99.35%
GSM8K	exact_match (flexible)	62.09%	70.51%	+8.42 pp	113.56%
Average	—

MMLU Category Breakdown

Table with columns: MMLU Category, Ornith-1.5-9B, Ornith-1.5-9B-MixedInt4-AutoRound, Difference, Recovery Rate
MMLU Category	Ornith-1.5-9B	Ornith-1.5-9B-MixedInt4-AutoRound	Difference	Recovery Rate
Humanities	69.12%	68.80%	-0.32 pp	99.54%
Other	81.62%	81.43%	-0.19 pp	99.77%
Social Sciences	85.93%	85.05%	-0.88 pp	98.98%

GSM8K

Table with columns: Metric, Ornith-1.5-9B, Ornith-1.5-9B-MixedInt4-AutoRound, Difference, Recovery Rate
Metric	Ornith-1.5-9B	Ornith-1.5-9B-MixedInt4-AutoRound	Difference	Recovery Rate
Flexible Exact Match	62.09%	70.51%	+8.42 pp	113.56%
Strict Exact Match	73.09%	73.69%	+0.60 pp	100.82%

Recovery Rate represents benchmark performance relative to the original Ornith-1.5-9B checkpoint. A recovery rate above 100% indicates that the quantized model scored higher than the original model in that particular evaluation. Benchmark preservation does not imply identical behavior for every prompt, multimodal workload, long-context workload, agentic workload, or generation setting.

Usage

This checkpoint is intended for inference engines with AutoRound quantization support, including compatible versions of vLLM.

Ornith-1.5-9B is a reasoning model and supports tool calling. Recent versions of the serving runtime are recommended.

vLLM

bash
vllm serve Pilcothink/Ornith-1.5-9B-MixedInt4-AutoRound \
  --served-model-name Ornith-1.5-9B-MixedInt4-AutoRound \
  --host 0.0.0.0 \
  --port 8000 \
  --trust-remote-code \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_xml \
  --enable-prefix-caching \
  --max-model-len 262144 \
  --gpu-memory-utilization 0.90

Recommended Sampling Parameters

For general tasks:

text
temperature=1.0
top_p=0.95
top_k=20
min_p=0.0
presence_penalty=1.5
repetition_penalty=1.0

For precise coding tasks:

text
temperature=0.6
top_p=0.95
top_k=20
min_p=0.0
presence_penalty=0.0
repetition_penalty=1.0

Long-Context Serving

The native context length of Ornith-1.5-9B is 262,144 tokens.

For workloads requiring a context window beyond the native limit, Ornith recommends extending the effective context using YaRN RoPE scaling.

Example for approximately 1M tokens:

bash
VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 \
vllm serve Pilcothink/Ornith-1.5-9B-MixedInt4-AutoRound \
  --served-model-name Ornith-1.5-9B-MixedInt4-AutoRound \
  --host 0.0.0.0 \
  --port 8000 \
  --trust-remote-code \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_xml \
  --enable-prefix-caching \
  --gpu-memory-utilization 0.90 \
  --hf-overrides '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":262144}}' \
  --max-model-len 1000000

Static YaRN scaling is applied to all requests and may slightly affect quality on shorter contexts. It should therefore only be enabled when workloads genuinely require context lengths beyond the native 262,144-token window.

Notes

This is a quantized derivative of ornith-ai/Ornith-1.5-9B.
The model uses a custom mixed-precision INT4 quantization recipe.
The vision components are preserved at their original precision.
The model retains the original Ornith reasoning and tool-calling format.
The native context length is 262,144 tokens.
Quantization may introduce small behavioral differences compared with the original checkpoint.
Long-context, multimodal, reasoning, and agentic workloads may respond differently from the original model even when benchmark recovery is high.

Acknowledgements

Base model: Ornith / DeepReinforce Team
Underlying architecture: Qwen3.5
Quantization framework: Intel AutoRound

Please refer to the original ornith-ai/Ornith-1.5-9B model card for licensing, intended usage, limitations, recommended sampling parameters, and other information applicable to the base model.

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Model Details

Model Provider

Pilcothink

Model Tree

Base

ornith-ai/Ornith-1.5-9B

Quantized

this model

Input Modalities

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Output Modalities

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Supported Functionality

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Base Model

Base model: ornith-ai/Ornith-1.5-9B

This model is a quantized version of the original Ornith-1.5-9B checkpoint. It is not a fine-tune, merge, or distillation.

Ornith-1.5-9B is a dense multimodal reasoning model based on the Qwen3.5 architecture.

Quantization

Quantization was performed using Intel AutoRound with a custom mixed-precision quantization configuration.

The quantization recipe was designed to balance:

Model quality
Memory efficiency
Inference performance
vLLM compatibility

Some model components are intentionally retained at higher precision where appropriate.

Table with columns: Property, Value
Property	Value
Quantization framework	Intel AutoRound
Quantization type	Custom Mixed-Precision INT4
Base model	ornith-ai/Ornith-1.5-9B
Architecture	Qwen3.5
Language layers	32
Vision encoder depth	27
Native context length	262,144 tokens
Vision tower	Preserved at original precision

The detailed mixed-precision allocation strategy is not included in this model card.

Evaluation

Evaluation was performed using AutoRound's evaluation interface with LM Evaluation Harness.

The following results compare the original Ornith-1.5-9B model against Ornith-1.5-9B-MixedInt4-AutoRound.

Table with columns: Benchmark, Metric, Ornith-1.5-9B, Ornith-1.5-9B-MixedInt4-AutoRound, Difference, Recovery Rate
Benchmark	Metric	Ornith-1.5-9B	Ornith-1.5-9B-MixedInt4-AutoRound	Difference	Recovery Rate
MMLU	acc	77.30%	76.80%	-0.50 pp	99.35%
GSM8K	exact_match (flexible)	62.09%	70.51%	+8.42 pp	113.56%
Average	—

MMLU Category Breakdown

Table with columns: MMLU Category, Ornith-1.5-9B, Ornith-1.5-9B-MixedInt4-AutoRound, Difference, Recovery Rate
MMLU Category	Ornith-1.5-9B	Ornith-1.5-9B-MixedInt4-AutoRound	Difference	Recovery Rate
Humanities	69.12%	68.80%	-0.32 pp	99.54%
Other	81.62%	81.43%	-0.19 pp	99.77%
Social Sciences	85.93%	85.05%	-0.88 pp	98.98%

GSM8K

Table with columns: Metric, Ornith-1.5-9B, Ornith-1.5-9B-MixedInt4-AutoRound, Difference, Recovery Rate
Metric	Ornith-1.5-9B	Ornith-1.5-9B-MixedInt4-AutoRound	Difference	Recovery Rate
Flexible Exact Match	62.09%	70.51%	+8.42 pp	113.56%
Strict Exact Match	73.09%	73.69%	+0.60 pp	100.82%

Recovery Rate represents benchmark performance relative to the original Ornith-1.5-9B checkpoint. A recovery rate above 100% indicates that the quantized model scored higher than the original model in that particular evaluation. Benchmark preservation does not imply identical behavior for every prompt, multimodal workload, long-context workload, agentic workload, or generation setting.

Usage

This checkpoint is intended for inference engines with AutoRound quantization support, including compatible versions of vLLM.

Ornith-1.5-9B is a reasoning model and supports tool calling. Recent versions of the serving runtime are recommended.

vLLM

bash
vllm serve Pilcothink/Ornith-1.5-9B-MixedInt4-AutoRound \
  --served-model-name Ornith-1.5-9B-MixedInt4-AutoRound \
  --host 0.0.0.0 \
  --port 8000 \
  --trust-remote-code \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_xml \
  --enable-prefix-caching \
  --max-model-len 262144 \
  --gpu-memory-utilization 0.90

Recommended Sampling Parameters

For general tasks:

text
temperature=1.0
top_p=0.95
top_k=20
min_p=0.0
presence_penalty=1.5
repetition_penalty=1.0

For precise coding tasks:

text
temperature=0.6
top_p=0.95
top_k=20
min_p=0.0
presence_penalty=0.0
repetition_penalty=1.0

Long-Context Serving

The native context length of Ornith-1.5-9B is 262,144 tokens.

For workloads requiring a context window beyond the native limit, Ornith recommends extending the effective context using YaRN RoPE scaling.

Example for approximately 1M tokens:

bash
VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 \
vllm serve Pilcothink/Ornith-1.5-9B-MixedInt4-AutoRound \
  --served-model-name Ornith-1.5-9B-MixedInt4-AutoRound \
  --host 0.0.0.0 \
  --port 8000 \
  --trust-remote-code \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_xml \
  --enable-prefix-caching \
  --gpu-memory-utilization 0.90 \
  --hf-overrides '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":262144}}' \
  --max-model-len 1000000

Notes

This is a quantized derivative of ornith-ai/Ornith-1.5-9B.
The model uses a custom mixed-precision INT4 quantization recipe.
The vision components are preserved at their original precision.
The model retains the original Ornith reasoning and tool-calling format.
The native context length is 262,144 tokens.
Quantization may introduce small behavioral differences compared with the original checkpoint.
Long-context, multimodal, reasoning, and agentic workloads may respond differently from the original model even when benchmark recovery is high.

Acknowledgements

Base model: Ornith / DeepReinforce Team
Underlying architecture: Qwen3.5
Quantization framework: Intel AutoRound

Please refer to the original ornith-ai/Ornith-1.5-9B model card for licensing, intended usage, limitations, recommended sampling parameters, and other information applicable to the base model.

Ornith-1.5-9B-MixedInt4-AutoRound

README

Base Model

Quantization

Evaluation

MMLU Category Breakdown

GSM8K

Usage

vLLM

Recommended Sampling Parameters

Long-Context Serving

Notes

Acknowledgements

Explore FriendliAI today

README

Base Model

Quantization

Evaluation

MMLU Category Breakdown

GSM8K

Usage

vLLM

Recommended Sampling Parameters

Long-Context Serving

Notes

Acknowledgements