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Model: chartreuse-verte/orb-human-typeahead-350m-v1.2 Source: Original Platform
2026-09-28 06:55:19 +08:00
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--- a/GGUF/orb-human-typeahead-350m-v1.2-Q8_0.gguf
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+---
+license: apache-2.0
+base_model: ibm-granite/granite-4.0-350m-base
+pipeline_tag: text-generation
+language:
+- en
+tags:
+- typeahead
+- autocomplete
+- roleplay
+- granite
+- gguf
+library_name: transformers
+---
+
+# orb-human-typeahead-350m-v1.2
+
+A 350M typeahead model that predicts **the human's next few words** in a
+roleplay chat — inline "ghost text" for the person typing, not a reply
+generator for the character. Full fine-tune of
+[ibm-granite/granite-4.0-350m-base](https://huggingface.co/ibm-granite/granite-4.0-350m-base).
+
+Most small LMs asked to continue a user's half-typed roleplay message produce
+fluent but irrelevant text. This model is trained specifically on
+(conversation context + partial user message → the words the user actually
+typed next), so its suggestions stay on-scene and in-voice.
+
+## Variants
+
+| Path | Format | Use with |
+|---|---|---|
+| `/` | safetensors bf16, plain `granite` arch | transformers |
+| `GGUF/orb-human-typeahead-350m-v1.2-Q8_0.gguf` | GGUF Q8_0 (default) | llama.cpp / llama-cpp-python |
+
+The original fine-tune used the `granitemoehybrid` architecture; since this
+variant is attention-only and dense, the weights are republished here as the
+equivalent plain `granite` architecture (logit-identical, verified), which
+loads everywhere without extras.
+
+## Prompt format
+
+Plain text, no chat template. Optional character summary, a marker line,
+name-prefixed turns, and finally the user's draft — the model continues the
+draft. Cut the suggestion at the first newline.
+
+```
+<character summary, optional>
+***Roleplay chat below***
+Sylvara: *She looks down from the watchtower and sees you.*
+Traveler: *I approach the encampment.*
+Sylvara: *She lowers her bow as you approach the gate.* "State your business, traveler."
+Traveler: *I raise both hands slowly and
+```
+
+A completion looks like ` step into the torchlight, keeping my voice low.*`
+
+Notes:
+- Trained to trigger at **word boundaries** only (draft ends on a whole word,
+  or on a trailing space). Mid-word completion is out of scope.
+- Trained context: up to ~4 recent turns, summaries ≤400 chars, turns ≤500 chars.
+
+## Serving recipe
+
+Greedy, short budget, stop at newline — mirrors how it was trained and evaluated:
+
+```python
+from llama_cpp import Llama
+
+llm = Llama("GGUF/orb-human-typeahead-350m-v1.2-Q8_0.gguf", n_ctx=1024)
+out = llm.create_completion(prompt, max_tokens=12, stop=["\n"], temperature=0.0)
+print(out["choices"][0]["text"])
+```
+
+Or with transformers:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tok = AutoTokenizer.from_pretrained("chartreuse-verte/orb-human-typeahead-350m-v1.2")
+model = AutoModelForCausalLM.from_pretrained("chartreuse-verte/orb-human-typeahead-350m-v1.2")
+ids = tok(prompt, return_tensors="pt").input_ids
+out = model.generate(ids, max_new_tokens=12, do_sample=False)
+print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True).split("\n")[0])
+```
+
+## Evaluation
+
+Scored on a held-out validation set of roleplay conversations (120
+prompts, conversation-disjoint from training; greedy, 12-token budget,
+suggestions cut at newline). word-EM@k = fraction of prompts where the first
+k words of the suggestion exactly match what the user really typed next;
+prefix-chars = mean length of the exactly-matching leading characters.
+
+| Metric | orb-human-typeahead-350m-v1.1 | this model |
+|---|---|---|
+| word-EM@1 | 0.333 | **0.358** |
+| word-EM@2 | 0.133 | **0.150** |
+| word-EM@3 | 0.075 | 0.075 |
+| prefix-chars | 3.92 | **4.12** |
+| completion perplexity | **8.83** | 9.00 |
+
+v1.2 is a data-only refresh of v1.1 (same 350M granite base) whose purpose was to
+flatten the completion distribution further — to stop the model defaulting to a small
+set of high-frequency phrasings. The validation set is itself drawn from the
+generator-styled distribution, so its gold completions over-represent exactly those
+defaults; a flatter model is expected to pay a small price here. v1.2 holds or gains
+on every exact-match column anyway (word-EM@1 0.333 → 0.358), with perplexity
+essentially unchanged (8.83 → 9.00).
+
+Read the table as a no-regression check on fluency and next-word accuracy, not as a
+quality ranking — exact agreement with one recorded continuation cannot score
+suggestion variety, and creative roleplay is high-entropy enough that many suggestions
+the metric counts as misses are perfectly good ones.
+
+## Training
+
+Two-stage full fine-tune of granite-4.0-350m-base: stage 1 on a mostly-synthetic
+roleplay corpus (self-chat generated to cover the on-scene, in-voice register real
+users type in), then stage 2 on a smaller private in-domain set in the serve-time
+prompt format (with stage-1 replay to limit forgetting). Over v1.1, stage 1's completion
+distribution was flattened further: a 3-gram repetition cap retires canned mid-phrase
+filler that the earlier first-word cap could not see, post-dialogue beats got real
+supervision instead of being capped into starvation, and generator punctuation tells
+(em-dashes, curly quotes) plus non-English leakage are normalized out rather than dropped. Examples are user turns split at word boundaries; loss is on
+the continuation. Suggestions are single-line by construction (completions end
+at newline).
+
+## Limitations
+
+- English-centric, roleplay register (asterisk actions, quoted dialogue).
+  Out of domain for assistant chat, code, or formal prose.
+- Roleplay corpora include mature themes; suggestions can reflect that.
+  Intended as a typing aid for consenting adult users of RP chat apps.
+- Not an instruction follower — it only continues drafts in the format above.
+- Suggests at word boundaries; won't complete a half-typed word.
--- a/config.json
+++ b/config.json
@@ -0,0 +1,34 @@
+{
+  "architectures": [
+    "GraniteForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attention_multiplier": 0.015625,
+  "bos_token_id": 100257,
+  "dtype": "bfloat16",
+  "embedding_multiplier": 12,
+  "eos_token_id": 100257,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 2048,
+  "logits_scaling": 4,
+  "max_position_embeddings": 32768,
+  "mlp_bias": false,
+  "model_type": "granite",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "pad_token_id": 100256,
+  "residual_multiplier": 0.263,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "rope_theta": 10000000,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.13.0",
+  "use_cache": true,
+  "vocab_size": 100352
+}
--- a/generation_config.json
+++ b/generation_config.json
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+{
+  "_from_model_config": true,
+  "bos_token_id": 100257,
+  "eos_token_id": 100257,
+  "output_attentions": false,
+  "output_hidden_states": false,
+  "pad_token_id": 100256,
+  "transformers_version": "5.13.0",
+  "use_cache": true
+}
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--- a/tokenizer.json
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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|end_of_text|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|end_of_text|>",
+  "is_local": true,
+  "local_files_only": false,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<|pad|>",
+  "padding_side": "left",
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": "<|unk|>"
+}