Minor Changes on Top of Isamus EXL2 PR (Docs, Enable FSM/CFG, Fix Tokenizer)

docs/reference/models/exllamav2.md

@@ -1,7 +1,15 @@
 # ExllamaV2
 
+The `outlines.models.exllamav2` model requires a Logits Processor component for compatibility with Outlines structured generation. While ExLlamaV2 doesn't natively support this feature, a third-party fork provides the necessary functionality. You can install it with the following command:
+
+```bash
+pip install git+https://github.com/lapp0/exllamav2@sampler-logits-processor
+```
+
+Install other requirements:
+
 ```bash
-pip install exllamav2 transformers torch
+pip install transformers torch
 ```
 
 *Coming soon*

outlines/generate/cfg.py

@@ -4,7 +4,7 @@
     SequenceGeneratorAdapter,
     VisionSequenceGeneratorAdapter,
 )
-from outlines.models import ExLlamaV2Model, LlamaCpp, OpenAI, TransformersVision
+from outlines.models import LlamaCpp, OpenAI, TransformersVision
 from outlines.samplers import Sampler, multinomial
 
 
@@ -41,13 +41,6 @@ def cfg_vision(model, cfg_str: str, sampler: Sampler = multinomial()):
     return VisionSequenceGeneratorAdapter(model, logits_processor, sampler)
 
 
-@cfg.register(ExLlamaV2Model)
-def cfg_exllamav2(model, cfg_str: str, sampler: Sampler = multinomial()):
-    raise NotImplementedError(
-        "Not yet available, track progress in https://github.com/dottxt-ai/outlines/pull/1010"
-    )
-
-
 @cfg.register(LlamaCpp)
 def cfg_llamacpp(model, cfg_str: str, sampler: Sampler = multinomial()):
     raise NotImplementedError("Not yet available due to bug in llama_cpp tokenizer")

outlines/generate/fsm.py

@@ -4,11 +4,10 @@
 
 from outlines.fsm.guide import RegexGuide
 from outlines.generate.api import (
-    SequenceGenerator,
     SequenceGeneratorAdapter,
     VisionSequenceGeneratorAdapter,
 )
-from outlines.models import ExLlamaV2Model, TransformersVision
+from outlines.models import TransformersVision
 from outlines.samplers import Sampler, multinomial
 
 
@@ -30,13 +29,3 @@ def fsm_vision(model, fsm: interegular.fsm.FSM, sampler: Sampler = multinomial()
     guide = RegexGuide.from_interegular_fsm(fsm, model.tokenizer)
     logits_processor = GuideLogitsProcessor(tokenizer=model.tokenizer, guide=guide)
     return VisionSequenceGeneratorAdapter(model, logits_processor, sampler)
-
-
-@fsm.register(ExLlamaV2Model)
-def fsm_exllamav2(
-    model, fsm: interegular.fsm.FSM, sampler: Sampler = multinomial()
-) -> SequenceGenerator:
-    fsm = RegexGuide.from_interegular_fsm(fsm, model.tokenizer)
-    device = model.device
-    generator = SequenceGenerator(fsm, model, sampler, device)
-    return generator

outlines/generate/regex.py

@@ -1,12 +1,10 @@
 from functools import singledispatch
 
-from outlines.fsm.guide import RegexGuide
 from outlines.generate.api import (
-    SequenceGenerator,
     SequenceGeneratorAdapter,
     VisionSequenceGeneratorAdapter,
 )
-from outlines.models import ExLlamaV2Model, OpenAI, TransformersVision
+from outlines.models import OpenAI, TransformersVision
 from outlines.samplers import Sampler, multinomial
 
 
@@ -49,20 +47,6 @@ def regex_vision(
     return VisionSequenceGeneratorAdapter(model, logits_processor, sampler)
 
 
-@regex.register(ExLlamaV2Model)
-def regex_exllamav2(
-    model,
-    regex_str: str,
-    sampler: Sampler = multinomial(),
-) -> SequenceGenerator:
-    fsm = RegexGuide(regex_str, model.tokenizer)
-
-    device = model.device
-    generator = SequenceGenerator(fsm, model, sampler, device)
-
-    return generator
-
-
 @regex.register(OpenAI)
 def regex_openai(
     model: OpenAI,

outlines/generate/text.py

@@ -1,12 +1,10 @@
 from functools import singledispatch
 
-from outlines.fsm.guide import StopAtEOSGuide
 from outlines.generate.api import (
-    SequenceGenerator,
     SequenceGeneratorAdapter,
     VisionSequenceGeneratorAdapter,
 )
-from outlines.models import ExLlamaV2Model, OpenAI, TransformersVision
+from outlines.models import OpenAI, TransformersVision
 from outlines.samplers import Sampler, multinomial
 
 
@@ -36,13 +34,6 @@ def text(model, sampler: Sampler = multinomial()) -> SequenceGeneratorAdapter:
     return SequenceGeneratorAdapter(model, None, sampler)
 
 
-@text.register(ExLlamaV2Model)
-def text_exllamav2(model, sampler: Sampler = multinomial()) -> SequenceGenerator:
-    fsm = StopAtEOSGuide(model.tokenizer)
-    device = model.device
-    return SequenceGenerator(fsm, model, sampler, device)
-
-
 @text.register(TransformersVision)
 def text_vision(model, sampler: Sampler = multinomial()):
     return VisionSequenceGeneratorAdapter(model, None, sampler)