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Model: jevonmao/llama31-8b-poker-mix-v1-step10k
Source: Original Platform
---
 .gitattributes                   |   36 +
 README.md                        |  263 ++++
 config.json                      |   40 +
 generation_config.json           |   12 +
 model-00001-of-00004.safetensors |    3 +
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 model.safetensors.index.json     |  298 +++++
 special_tokens_map.json          |   16 +
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 tokenizer_config.json            | 2062 ++++++++++++++++++++++++++++++
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diff --git a/README.md b/README.md
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+---
+library_name: transformers
+license: llama3.1
+base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
+datasets:
+  - jevonmao/poker-sft-mix-v1
+language:
+  - en
+pipeline_tag: text-generation
+tags:
+  - poker
+  - heads-up-no-limit
+  - texas-holdem
+  - gto
+  - nash-equilibrium
+  - lora
+  - distillation
+  - llama
+  - llama-3.1
+  - sft
+  - reasoning
+model-index:
+  - name: PokerLlama-4
+    results:
+      - task:
+          type: text-classification
+          name: Heads-Up No-Limit action prediction
+        dataset:
+          type: jevonmao/poker-sft-mix-v1
+          name: HoldemEval-31k (held-out split)
+        metrics:
+          - type: accuracy
+            value: 0.8396
+            name: Top-1 action-type accuracy
+          - type: parse-rate
+            value: 1.0
+            name: Output parse rate
+---
+
+# PokerLlama-4
+
+**PokerLlama-4** is a supervised fine-tune of **Llama-3.1-8B-Instruct** for heads-up
+no-limit Texas hold'em at 200 big blinds. It distills the GTO Wizard equilibrium
+solver's mixed strategies into a chat-template language model and supports both
+direct action emission and chain-of-thought reasoning at inference time.
+
+It reaches **83.96% top-1 action-type accuracy** on a 31,105-decision held-out
+evaluation split — a new project record and the highest of any 8B model we
+evaluated, outperforming both open-source poker fine-tunes and zero-shot
+frontier reasoning models on postflop action prediction.
+
+- **Project landing page:** https://jevonmao.github.io/cs153-project/
+- **Code & evaluation harness:** https://github.com/jevonmao/cs153-project
+- **Training corpus:** [`jevonmao/poker-sft-mix-v1`](https://huggingface.co/datasets/jevonmao/poker-sft-mix-v1)
+
+> *PokerLlama-4: When Distilling a Nash Policy Beats the Benchmark but Loses the Game.*
+> Stanford CS 153 Final Project, 2026.
+
+## Model details
+
+| Field | Value |
+|---|---|
+| Base model | `meta-llama/Meta-Llama-3.1-8B-Instruct` |
+| Adapter | LoRA, rank 128, α = 256, dropout 0.05 |
+| Target modules | All seven linear projections per transformer block |
+| Trainable parameters | ≈ 335M (4.2% of base) |
+| Precision | bfloat16, merged FP16 weights pushed |
+| Optimizer | Paged 8-bit AdamW |
+| LR schedule | Cosine, 2% warmup, peak η = 2 × 10⁻⁴ |
+| Epochs | 1 over 606,630 records (≈ 19,700 steps) |
+| Effective batch | 32 (4 GPU × 8 per-device × 1 accumulation) |
+| Hardware | 4 × H100 80 GB |
+| Wall-clock | ≈ 6 hours |
+| Eval-loss-converged checkpoint | step 10,000 (eval loss 0.0873) |
+
+This repository contains the merged FP16 weights at the converged checkpoint.
+No LoRA adapter file is included — the merge is in-place, so the model loads
+exactly like the base via `AutoModelForCausalLM`.
+
+## Intended use
+
+PokerLlama-4 is a research artifact accompanying the project's final report.
+It is intended for:
+
+- **Reproducing the project's evaluation results** on the held-out
+  HoldemEval-31k split and live-play benchmarks.
+- **Studying GTO-policy distillation** into small language models — the
+  trade-off between matching the equilibrium action distribution offline and
+  achieving expected value against opponents who deviate from equilibrium.
+- **Tool-use / function-call experiments** in a poker domain — the model emits
+  well-formed `preflop_gto` tool calls with 99.9% argument correctness.
+
+It is **not** intended for gambling, real-money play, or any context in which
+the output is acted on without human oversight.
+
+## How to use
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "jevonmao/llama31-8b-poker-mix-v1-step10k"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+SYSTEM_PROMPT = (
+    "You are an expert No Limit Texas Hold'em poker assistant. "
+    "Read the hand history carefully and decide the next action. "
+    "Respond with a single action wrapped in <action></action> tags."
+)
+
+USER_PROMPT = """\
+Game: HUNL 200BB
+Your position: SB
+Your hand: As Kh
+Pot: 150
+Effective stack: 19,900
+Action so far: SB posts 50, BB posts 100.
+
+Legal actions: fold, call 100, raise [to] [200..19900]
+"""
+
+messages = [
+    {"role": "system", "content": SYSTEM_PROMPT},
+    {"role": "user",   "content": USER_PROMPT},
+]
+
+inputs = tokenizer.apply_chat_template(
+    messages, return_tensors="pt", add_generation_prompt=True
+).to(model.device)
+
+with torch.inference_mode():
+    out = model.generate(inputs, max_new_tokens=32, do_sample=False)
+
+print(tokenizer.decode(out[0, inputs.shape[1]:], skip_special_tokens=True))
+# e.g. <action>raise to 225</action>
+```
+
+For chain-of-thought inference, replace the system prompt's final sentence with
+the project's CoT mode marker (see `eval/` in the source repository).
+
+### Greedy decoding is recommended
+
+All reported results use greedy decoding (`do_sample=False`). Temperature
+sampling at `T = 1.0` recovers the mixed-strategy character of the distilled
+policy at the cost of action-label accuracy and is appropriate for live play
+against opponents who themselves play balanced strategies.
+
+## Training data
+
+PokerLlama-4 is fine-tuned on
+[`jevonmao/poker-sft-mix-v1`](https://huggingface.co/datasets/jevonmao/poker-sft-mix-v1),
+a 606,630-record four-way mix of equilibrium-solver decisions and audited
+chain-of-thought reasoning traces:
+
+| Slice | Records | Source | Mode |
+|---|---:|---|---|
+| `A_cot`    | 248,931 | GTO Wizard interaction split | Chain-of-thought |
+| `A_direct` | 248,931 | GTO Wizard interaction split | Direct action |
+| `B_cot`    |  54,384 | CFR postflop solver, audited | Chain-of-thought |
+| `B_direct` |  54,384 | CFR postflop solver, audited | Direct action |
+
+A teacher-rationale hand-class audit rejects 21.7% of solver-source CoT
+records (15,054 / 69,438) for categorical hand-class errors before training.
+
+## Evaluation
+
+### Offline action accuracy (HoldemEval-31k)
+
+| Model | n | Parse % | Type accuracy |
+|---|---:|---:|---:|
+| `meta-llama/Meta-Llama-3.1-8B-Instruct` | 31,105 | 80.84% | 28.75% |
+| `YiPz/llama3-8b-pokerbench-sft` | 31,105 | 96.72% | 30.63% |
+| PokerLlama-2 (postflop specialist, predecessor) | 31,105 | 100% | 55.82% |
+| PokerLlama-1 (predecessor) | 31,105 | 100% | 61.62% |
+| **PokerLlama-4 (this model)** | **31,105** | **100%** | **83.96%** |
+
+The +22.3 pt margin over the closest 8B baseline and the +53.3 pt margin over
+the published PokerBench-8B fine-tune are isolated to the corpus and training
+recipe — every model shares the Llama-3.1-8B backbone.
+
+### Against frontier reasoning models (postflop split, n = 2,044)
+
+A separate predecessor checkpoint (PokerLlama-2, a postflop specialist on the
+same project line) was evaluated against three frontier reasoning baselines on
+a 2,044-decision postflop-only split:
+
+| Model | Mode | Parse % | Type accuracy |
+|---|---|---:|---:|
+| DeepSeek-V4-Pro | Reasoning trace | 99.6% | 62.9% |
+| GPT-4.1 | Zero-shot | 98.8% | 68.8% |
+| GPT-5.4-pro | Medium reasoning effort | 90.7% | 68.5% |
+| PokerLlama-2 (8B specialized) | Fine-tune | 100% | **76.4%** |
+
+Specialized distillation clears the frontier zero-shot ceiling (≈ 69%) on
+postflop action prediction at full parse-rate reliability.
+
+### Live self-play (rlcard heads-up 200 BB)
+
+| Opponent | n hands | bb/100 | 95% CI |
+|---|---:|---:|---|
+| `rule:fold` | 4,000 | +74.0 | [+73, +75] |
+| `rule:allin` | 4,000 | +448.3 | [+277, +620] |
+| `rule:random` | 4,000 | +166.5 | [+63, +270] |
+| `PokerBench-8B` | 8,000 | −21.4 | [−36, −7] |
+| `Llama-3.1-8B-Instruct` (base) | 7,085 | −39.6 | [−52, −27] |
+
+Greedy decoding of the distilled policy approximates the modal action of the
+solver's mixed strategy. Against a balanced opponent this is approximately
+Nash; against opponents who deviate from equilibrium (PokerBench-8B
+over-folds, base Llama rarely folds) the modal-of-mixed-strategy policy does
+not maximize EV. This trade-off is the central finding of the accompanying
+report — see §Diagnostics on the project landing page for the full analysis.
+
+## Limitations
+
+- **Mixed-strategy mean targeting.** The training objective drives the model
+  toward the equilibrium policy. Greedy decoding therefore picks the modal
+  equilibrium action, which is not always the EV-maximizing response to a
+  non-equilibrium opponent. This is a feature for Nash-quality evaluation and
+  a constraint for live-play exploitation.
+- **Bet-space discretization.** The live-play simulator discretizes bets into
+  five canonical sizes; the training distribution covers a wider continuous
+  range. Behavior on bet sizes outside the simulator's discretization may not
+  reflect what the model would do given a continuous-bet interface.
+- **Game format scope.** The model is trained exclusively on heads-up no-limit
+  Texas hold'em at 200 BB depth. It has not been evaluated at other stack
+  depths, other player counts, or other variants.
+- **Teacher rationales on the GTO Wizard slice.** The hand-class audit applies
+  only to the solver chain-of-thought source; the GTO Wizard chain-of-thought
+  slice has no per-spot hand-class ground truth and is left unaudited. A
+  residual teacher-error rate is assumed.
+
+## Responsible use
+
+This model is a research artifact for a coursework project. It is **not**
+intended for use in real-money gambling or in any setting where a
+mis-prediction could cause financial harm. The Llama-3.1 community license
+governs downstream use of the base weights and applies to this fine-tune in
+full.
+
+## Citation
+
+```bibtex
+@misc{mao2026pokerllama,
+  title  = {PokerLlama-4: When Distilling a Nash Policy Beats the Benchmark but Loses the Game},
+  author = {Mao, Jevon},
+  year   = {2026},
+  note   = {Stanford CS 153 Final Project},
+  url    = {https://jevonmao.github.io/cs153-project/}
+}
+```
+
+## Acknowledgements
+
+- **GTO Wizard** for the live solver-decision data interface.
+- **Meta** for releasing Llama-3.1-8B-Instruct.
+- **Stanford CS 153** course staff for compute access on the H100 cluster.
diff --git a/config.json b/config.json
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--- /dev/null
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+{
+  "_name_or_path": "meta-llama/Meta-Llama-3.1-8B-Instruct",
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+  ],
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+    128008,
+    128009
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+  "hidden_act": "silu",
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+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.45.2",
+  "use_cache": true,
+  "vocab_size": 128256
+}
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+  },
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n    {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n            {%- for arg_name, arg_val in tool_call.arguments | items %}\n                {{- arg_name + '=\"' + arg_val + '\"' }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- endif %}\n                {%- endfor %}\n            {{- \")\" }}\n        {%- else  %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n            {{- '\"parameters\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- \"}\" }}\n        {%- endif %}\n        {%- if builtin_tools is defined %}\n            {#- This means we're in ipython mode #}\n            {{- \"<|eom_id|>\" }}\n        {%- else %}\n            {{- \"<|eot_id|>\" }}\n        {%- endif %}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}