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xanguera/phonellm-alpha-1-int8
phonellm-alpha-1-int8 is a text generation model from xanguera. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as bsd-2-clause.
A community INT8 quantization of pipecat-ai/phonellm-alpha-1, built to fit Ampere GPUs (e.g. 3x RTX 3090) that lack NVFP4 support and can't hold the full BF16 checkpoint alongside a usable KV cache.
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From the Hugging Face model README
A community INT8 quantization of pipecat-ai/phonellm-alpha-1, built to fit Ampere GPUs (e.g. 3x RTX 3090) that lack NVFP4 support and can't hold the full BF16 checkpoint alongside a usable KV cache.
| Base model | pipecat-ai/phonellm-alpha-1 (Nemotron 3 Nano 30B-A3B hybrid Mamba-Transformer MoE, fine-tuned for voice agents) |
| Quantization format | compressed-tensors, INT8 |
| Weights | int8, symmetric, per-channel |
| Activations | int8, symmetric, dynamic per-token |
| Kept in BF16 | attention/Mamba mixer projections, gates, norms, lm_head |
| MoE experts | INT8 via RTN — this architecture's forward pass structurally prevents GPTQ calibration from reaching per-expert modules (a known limitation, not a bug) |
| Size on disk | ~32GB (vs. ~59GB BF16) |
| Serving | vLLM, trust_remote_code=True |
| Validated on | 3x RTX 3090, tensor_parallel_size=2 |
Neither official PhoneLLM Alpha 1 release fits 3x RTX 3090: they're Ampere (no NVFP4 support), and the BF16 checkpoint alone is ~59GB — too tight for KV cache headroom across only 3x24GB cards. This checkpoint quantizes the MoE experts and general Linear layers to INT8 (channel-wise symmetric weights, dynamic per-token symmetric int8 activations) while keeping attention/Mamba projections, norms, gates, and lm_head in BF16, matching NVIDIA's own precedent for this architecture.
Coherent generations, and correct <tool_call> emission with accurate parameter extraction, confirmed via a say/do test. This checkpoint has not been run against PhoneBench — no formal accuracy comparison against the BF16 or NVFP4 releases is published here.
vllm serve xanguera/phonellm-alpha-1-int8 \
--trust-remote-code \
--tensor-parallel-size 2 \
--max-model-len 8192 \
--reasoning-parser-plugin nano_v3_reasoning_parser.py \
--reasoning-parser nano_v3
Important: the reasoning-parser plugin is required. Without it, the model's <think>...</think> reasoning text leaks directly into the visible response content (a literal </think> shows up mid-sentence) instead of landing in a separate reasoning field — setting enable_thinking: false via chat_template_kwargs alone is not sufficient. This only shows up when testing the actual served HTTP endpoint; vLLM's offline batch API doesn't exercise this code path.
Recommended inference settings match the base model: temperature=0, thinking disabled.
This model is a quantization of pipecat-ai/phonellm-alpha-1, released under the BSD 2-Clause License, itself a derivative of NVIDIA Nemotron 3 Nano 30B-A3B, licensed under the NVIDIA Nemotron Open Model License.
Per Section 3 (Redistribution) of the NVIDIA Nemotron Open Model License, this repository includes a copy of that license and retains the NVIDIA copyright and attribution notices carried in the bundled model code. This quantization is released under the BSD 2-Clause License, consistent with the terms under which the base model was released; the NVIDIA license continues to apply to the underlying Nemotron work. "Nemotron" and "NVIDIA" are trademarks of NVIDIA Corporation, used here only to describe the origin of the base model.
Built on PhoneLLM Alpha 1 by the Pipecat team at Daily, itself built on NVIDIA Nemotron 3 Nano 30B-A3B.