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Model: jevonmao/llama31-8b-poker-mix-v1-step10k Source: Original Platform
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---
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library_name: transformers
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license: llama3.1
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base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
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datasets:
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- jevonmao/poker-sft-mix-v1
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language:
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- en
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pipeline_tag: text-generation
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tags:
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- poker
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- heads-up-no-limit
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- texas-holdem
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- gto
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- nash-equilibrium
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- lora
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- distillation
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- llama
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- llama-3.1
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- sft
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- reasoning
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model-index:
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- name: PokerLlama-4
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results:
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- task:
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type: text-classification
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name: Heads-Up No-Limit action prediction
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dataset:
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type: jevonmao/poker-sft-mix-v1
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name: HoldemEval-31k (held-out split)
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metrics:
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- type: accuracy
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value: 0.8396
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name: Top-1 action-type accuracy
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- type: parse-rate
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value: 1.0
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name: Output parse rate
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---
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# PokerLlama-4
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**PokerLlama-4** is a supervised fine-tune of **Llama-3.1-8B-Instruct** for heads-up
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no-limit Texas hold'em at 200 big blinds. It distills the GTO Wizard equilibrium
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solver's mixed strategies into a chat-template language model and supports both
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direct action emission and chain-of-thought reasoning at inference time.
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It reaches **83.96% top-1 action-type accuracy** on a 31,105-decision held-out
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evaluation split — a new project record and the highest of any 8B model we
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evaluated, outperforming both open-source poker fine-tunes and zero-shot
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frontier reasoning models on postflop action prediction.
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- **Project landing page:** https://jevonmao.github.io/cs153-project/
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- **Code & evaluation harness:** https://github.com/jevonmao/cs153-project
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- **Training corpus:** [`jevonmao/poker-sft-mix-v1`](https://huggingface.co/datasets/jevonmao/poker-sft-mix-v1)
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> *PokerLlama-4: When Distilling a Nash Policy Beats the Benchmark but Loses the Game.*
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> Stanford CS 153 Final Project, 2026.
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## Model details
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| Field | Value |
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|---|---|
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| Base model | `meta-llama/Meta-Llama-3.1-8B-Instruct` |
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| Adapter | LoRA, rank 128, α = 256, dropout 0.05 |
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| Target modules | All seven linear projections per transformer block |
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| Trainable parameters | ≈ 335M (4.2% of base) |
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| Precision | bfloat16, merged FP16 weights pushed |
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| Optimizer | Paged 8-bit AdamW |
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| LR schedule | Cosine, 2% warmup, peak η = 2 × 10⁻⁴ |
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| Epochs | 1 over 606,630 records (≈ 19,700 steps) |
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| Effective batch | 32 (4 GPU × 8 per-device × 1 accumulation) |
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| Hardware | 4 × H100 80 GB |
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| Wall-clock | ≈ 6 hours |
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| Eval-loss-converged checkpoint | step 10,000 (eval loss 0.0873) |
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This repository contains the merged FP16 weights at the converged checkpoint.
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No LoRA adapter file is included — the merge is in-place, so the model loads
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exactly like the base via `AutoModelForCausalLM`.
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## Intended use
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PokerLlama-4 is a research artifact accompanying the project's final report.
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It is intended for:
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- **Reproducing the project's evaluation results** on the held-out
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HoldemEval-31k split and live-play benchmarks.
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- **Studying GTO-policy distillation** into small language models — the
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trade-off between matching the equilibrium action distribution offline and
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achieving expected value against opponents who deviate from equilibrium.
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- **Tool-use / function-call experiments** in a poker domain — the model emits
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well-formed `preflop_gto` tool calls with 99.9% argument correctness.
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It is **not** intended for gambling, real-money play, or any context in which
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the output is acted on without human oversight.
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## How to use
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "jevonmao/llama31-8b-poker-mix-v1-step10k"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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SYSTEM_PROMPT = (
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"You are an expert No Limit Texas Hold'em poker assistant. "
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"Read the hand history carefully and decide the next action. "
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"Respond with a single action wrapped in <action></action> tags."
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)
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USER_PROMPT = """\
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Game: HUNL 200BB
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Your position: SB
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Your hand: As Kh
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Pot: 150
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Effective stack: 19,900
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Action so far: SB posts 50, BB posts 100.
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Legal actions: fold, call 100, raise [to] [200..19900]
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"""
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": USER_PROMPT},
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]
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inputs = tokenizer.apply_chat_template(
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messages, return_tensors="pt", add_generation_prompt=True
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).to(model.device)
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with torch.inference_mode():
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out = model.generate(inputs, max_new_tokens=32, do_sample=False)
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print(tokenizer.decode(out[0, inputs.shape[1]:], skip_special_tokens=True))
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# e.g. <action>raise to 225</action>
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```
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For chain-of-thought inference, replace the system prompt's final sentence with
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the project's CoT mode marker (see `eval/` in the source repository).
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### Greedy decoding is recommended
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All reported results use greedy decoding (`do_sample=False`). Temperature
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sampling at `T = 1.0` recovers the mixed-strategy character of the distilled
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policy at the cost of action-label accuracy and is appropriate for live play
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against opponents who themselves play balanced strategies.
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## Training data
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PokerLlama-4 is fine-tuned on
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[`jevonmao/poker-sft-mix-v1`](https://huggingface.co/datasets/jevonmao/poker-sft-mix-v1),
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a 606,630-record four-way mix of equilibrium-solver decisions and audited
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chain-of-thought reasoning traces:
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| Slice | Records | Source | Mode |
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|---|---:|---|---|
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| `A_cot` | 248,931 | GTO Wizard interaction split | Chain-of-thought |
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| `A_direct` | 248,931 | GTO Wizard interaction split | Direct action |
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| `B_cot` | 54,384 | CFR postflop solver, audited | Chain-of-thought |
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| `B_direct` | 54,384 | CFR postflop solver, audited | Direct action |
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A teacher-rationale hand-class audit rejects 21.7% of solver-source CoT
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records (15,054 / 69,438) for categorical hand-class errors before training.
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## Evaluation
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### Offline action accuracy (HoldemEval-31k)
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| Model | n | Parse % | Type accuracy |
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|---|---:|---:|---:|
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| `meta-llama/Meta-Llama-3.1-8B-Instruct` | 31,105 | 80.84% | 28.75% |
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| `YiPz/llama3-8b-pokerbench-sft` | 31,105 | 96.72% | 30.63% |
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| PokerLlama-2 (postflop specialist, predecessor) | 31,105 | 100% | 55.82% |
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| PokerLlama-1 (predecessor) | 31,105 | 100% | 61.62% |
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| **PokerLlama-4 (this model)** | **31,105** | **100%** | **83.96%** |
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The +22.3 pt margin over the closest 8B baseline and the +53.3 pt margin over
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the published PokerBench-8B fine-tune are isolated to the corpus and training
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recipe — every model shares the Llama-3.1-8B backbone.
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### Against frontier reasoning models (postflop split, n = 2,044)
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A separate predecessor checkpoint (PokerLlama-2, a postflop specialist on the
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same project line) was evaluated against three frontier reasoning baselines on
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a 2,044-decision postflop-only split:
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| Model | Mode | Parse % | Type accuracy |
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|---|---|---:|---:|
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| DeepSeek-V4-Pro | Reasoning trace | 99.6% | 62.9% |
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| GPT-4.1 | Zero-shot | 98.8% | 68.8% |
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| GPT-5.4-pro | Medium reasoning effort | 90.7% | 68.5% |
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| PokerLlama-2 (8B specialized) | Fine-tune | 100% | **76.4%** |
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Specialized distillation clears the frontier zero-shot ceiling (≈ 69%) on
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postflop action prediction at full parse-rate reliability.
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### Live self-play (rlcard heads-up 200 BB)
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| Opponent | n hands | bb/100 | 95% CI |
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|---|---:|---:|---|
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| `rule:fold` | 4,000 | +74.0 | [+73, +75] |
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| `rule:allin` | 4,000 | +448.3 | [+277, +620] |
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| `rule:random` | 4,000 | +166.5 | [+63, +270] |
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| `PokerBench-8B` | 8,000 | −21.4 | [−36, −7] |
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| `Llama-3.1-8B-Instruct` (base) | 7,085 | −39.6 | [−52, −27] |
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Greedy decoding of the distilled policy approximates the modal action of the
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solver's mixed strategy. Against a balanced opponent this is approximately
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Nash; against opponents who deviate from equilibrium (PokerBench-8B
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over-folds, base Llama rarely folds) the modal-of-mixed-strategy policy does
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not maximize EV. This trade-off is the central finding of the accompanying
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report — see §Diagnostics on the project landing page for the full analysis.
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## Limitations
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- **Mixed-strategy mean targeting.** The training objective drives the model
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toward the equilibrium policy. Greedy decoding therefore picks the modal
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equilibrium action, which is not always the EV-maximizing response to a
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non-equilibrium opponent. This is a feature for Nash-quality evaluation and
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a constraint for live-play exploitation.
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- **Bet-space discretization.** The live-play simulator discretizes bets into
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five canonical sizes; the training distribution covers a wider continuous
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range. Behavior on bet sizes outside the simulator's discretization may not
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reflect what the model would do given a continuous-bet interface.
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- **Game format scope.** The model is trained exclusively on heads-up no-limit
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Texas hold'em at 200 BB depth. It has not been evaluated at other stack
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depths, other player counts, or other variants.
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- **Teacher rationales on the GTO Wizard slice.** The hand-class audit applies
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only to the solver chain-of-thought source; the GTO Wizard chain-of-thought
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slice has no per-spot hand-class ground truth and is left unaudited. A
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residual teacher-error rate is assumed.
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## Responsible use
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This model is a research artifact for a coursework project. It is **not**
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intended for use in real-money gambling or in any setting where a
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mis-prediction could cause financial harm. The Llama-3.1 community license
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governs downstream use of the base weights and applies to this fine-tune in
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full.
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## Citation
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```bibtex
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@misc{mao2026pokerllama,
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title = {PokerLlama-4: When Distilling a Nash Policy Beats the Benchmark but Loses the Game},
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author = {Mao, Jevon},
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year = {2026},
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note = {Stanford CS 153 Final Project},
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url = {https://jevonmao.github.io/cs153-project/}
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}
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```
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## Acknowledgements
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- **GTO Wizard** for the live solver-decision data interface.
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- **Meta** for releasing Llama-3.1-8B-Instruct.
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- **Stanford CS 153** course staff for compute access on the H100 cluster.
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40
config.json
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config.json
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{
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"_name_or_path": "meta-llama/Meta-Llama-3.1-8B-Instruct",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
|
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"eos_token_id": [
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128009
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|
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"head_dim": 128,
|
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"hidden_act": "silu",
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"hidden_size": 4096,
|
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"initializer_range": 0.02,
|
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"intermediate_size": 14336,
|
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"max_position_embeddings": 131072,
|
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"mlp_bias": false,
|
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"model_type": "llama",
|
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"num_attention_heads": 32,
|
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"num_hidden_layers": 32,
|
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"num_key_value_heads": 8,
|
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"pretraining_tp": 1,
|
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"rms_norm_eps": 1e-05,
|
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"rope_scaling": {
|
||||
"factor": 8.0,
|
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"high_freq_factor": 4.0,
|
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"low_freq_factor": 1.0,
|
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"original_max_position_embeddings": 8192,
|
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"rope_type": "llama3"
|
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},
|
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"rope_theta": 500000.0,
|
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"tie_word_embeddings": false,
|
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"torch_dtype": "bfloat16",
|
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"transformers_version": "4.45.2",
|
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"use_cache": true,
|
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"vocab_size": 128256
|
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}
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generation_config.json
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|
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"bos_token_id": 128000,
|
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"do_sample": true,
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|
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"temperature": 0.6,
|
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"top_p": 0.9,
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"transformers_version": "4.45.2"
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}
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||||
}
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||||
16
special_tokens_map.json
Normal file
16
special_tokens_map.json
Normal file
@@ -0,0 +1,16 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
2062
tokenizer_config.json
Normal file
2062
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user